Compliance monitoring event-based driving of an orchestrator by a storage system

ABSTRACT

An illustrative method includes a storage management system detecting an event within a storage system, determining, based on the event, an operation related to a compliance ruleset associated with a compliance policy, and providing a notification of the operation to an orchestration system configured to manage an execution of the operation by a computing system associated with the storage system.

RELATED APPLICATIONS

This application is a continuation-in-part of U.S. patent applicationSer. No. 17/318,866, filed May 12, 2021, which application isincorporated herein by reference in its entirety.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings illustrate various embodiments and are a partof the specification. The illustrated embodiments are merely examplesand do not limit the scope of the disclosure. Throughout the drawings,identical or similar reference numbers designate identical or similarelements.

FIG. 1A illustrates a first example system for data storage inaccordance with some implementations.

FIG. 1B illustrates a second example system for data storage inaccordance with some implementations.

FIG. 1C illustrates a third example system for data storage inaccordance with some implementations.

FIG. 1D illustrates a fourth example system for data storage inaccordance with some implementations.

FIG. 2A is a perspective view of a storage cluster with multiple storagenodes and internal storage coupled to each storage node to providenetwork attached storage, in accordance with some embodiments.

FIG. 2B is a block diagram showing an interconnect switch couplingmultiple storage nodes in accordance with some embodiments.

FIG. 2C is a multiple level block diagram, showing contents of a storagenode and contents of one of the non-volatile solid state storage unitsin accordance with some embodiments.

FIG. 2D shows a storage server environment, which uses embodiments ofthe storage nodes and storage units of some previous figures inaccordance with some embodiments.

FIG. 2E is a blade hardware block diagram, showing a control plane,compute and storage planes, and authorities interacting with underlyingphysical resources, in accordance with some embodiments.

FIG. 2F depicts elasticity software layers in blades of a storagecluster, in accordance with some embodiments.

FIG. 2G depicts authorities and storage resources in blades of a storagecluster, in accordance with some embodiments.

FIG. 3A sets forth a diagram of a storage system that is coupled fordata communications with a cloud services provider in accordance withsome embodiments of the present disclosure.

FIG. 3B sets forth a diagram of a storage system in accordance with someembodiments of the present disclosure.

FIG. 3C sets forth an example of a cloud-based storage system inaccordance with some embodiments of the present disclosure.

FIG. 3D illustrates an exemplary computing device that may bespecifically configured to perform one or more of the processesdescribed herein.

FIG. 4 sets forth a diagram of a monitoring system in accordance withsome embodiments.

FIGS. 5A and 5B set forth example diagrams illustrating a monitoringsystem associated with a storage system in accordance with someembodiments.

FIG. 6 illustrates an exemplary method in accordance with someembodiments.

FIG. 7 illustrates an exemplary training stage of a machine learningmodel configured to generate an attribute model in accordance with someembodiments.

FIGS. 8A and 8B illustrate example systems that implement a monitoringsystem in accordance with some embodiments of the present disclosure.

FIG. 9 illustrates an example cluster of a container system inaccordance with some embodiments of the present disclosure.

FIG. 10 illustrates an example worker node of a container system inaccordance with some embodiments of the present disclosure.

FIG. 11 illustrates an exemplary method in accordance with someembodiments of the present disclosure.

DESCRIPTION OF EMBODIMENTS

Example methods, apparatuses, and products for monitoring compliance ofa dataset with a compliance ruleset associated with a compliance policyin accordance with embodiments of the present disclosure are describedwith reference to the accompanying drawings, beginning with FIG. 1A.FIG. 1A illustrates an example system for data storage, in accordancewith some implementations. System 100 (also referred to as “storagesystem” herein) includes numerous elements for purposes of illustrationrather than limitation. It may be noted that system 100 may include thesame, more, or fewer elements configured in the same or different mannerin other implementations.

System 100 includes a number of computing devices 164A-B. Computingdevices (also referred to as “client devices” herein) may be embodied,for example, a server in a data center, a workstation, a personalcomputer, a notebook, or the like. Computing devices 164A-B may becoupled for data communications to one or more storage arrays 102A-Bthrough a storage area network (‘SAN’) 158 or a local area network(‘LAN’) 160.

The SAN 158 may be implemented with a variety of data communicationsfabrics, devices, and protocols. For example, the fabrics for SAN 158may include Fibre Channel, Ethernet, Infiniband, Serial Attached SmallComputer System Interface (‘SAS’), or the like. Data communicationsprotocols for use with SAN 158 may include Advanced TechnologyAttachment (‘ATA’), Fibre Channel Protocol, Small Computer SystemInterface (‘SCSI’), Internet Small Computer System Interface (‘iSCSI’),HyperSCSI, Non-Volatile Memory Express (‘NVMe’) over Fabrics, or thelike. It may be noted that SAN 158 is provided for illustration, ratherthan limitation. Other data communication couplings may be implementedbetween computing devices 164A-B and storage arrays 102A-B.

The LAN 160 may also be implemented with a variety of fabrics, devices,and protocols. For example, the fabrics for LAN 160 may include Ethernet(802.3), wireless (802.11), or the like. Data communication protocolsfor use in LAN 160 may include Transmission Control Protocol (‘TCP’),User Datagram Protocol (‘UDP’), Internet Protocol (‘IP’), HyperTextTransfer Protocol (‘HTTP’), Wireless Access Protocol (‘WAP’), HandheldDevice Transport Protocol (‘HDTP’), Session Initiation Protocol (‘SIP’),Real Time Protocol (‘RTP’), or the like.

Storage arrays 102A-B may provide persistent data storage for thecomputing devices 164A-B. Storage array 102A may be contained in achassis (not shown), and storage array 102B may be contained in anotherchassis (not shown), in implementations. Storage array 102A and 102B mayinclude one or more storage array controllers 110A-D (also referred toas “controller” herein). A storage array controller 110A-D may beembodied as a module of automated computing machinery comprisingcomputer hardware, computer software, or a combination of computerhardware and software. In some implementations, the storage arraycontrollers 110A-D may be configured to carry out various storage tasks.Storage tasks may include writing data received from the computingdevices 164A-B to storage array 102A-B, erasing data from storage array102A-B, retrieving data from storage array 102A-B and providing data tocomputing devices 164A-B, monitoring and reporting of disk utilizationand performance, performing redundancy operations, such as RedundantArray of Independent Drives (‘RAID’) or RAID-like data redundancyoperations, compressing data, encrypting data, and so forth.

Storage array controller 110A-D may be implemented in a variety of ways,including as a Field Programmable Gate Array (‘FPGA’), a ProgrammableLogic Chip (‘PLC’), an Application Specific Integrated Circuit (‘ASIC’),System-on-Chip (‘SOC’), or any computing device that includes discretecomponents such as a processing device, central processing unit,computer memory, or various adapters. Storage array controller 110A-Dmay include, for example, a data communications adapter configured tosupport communications via the SAN 158 or LAN 160. In someimplementations, storage array controller 110A-D may be independentlycoupled to the LAN 160. In implementations, storage array controller110A-D may include an I/O controller or the like that couples thestorage array controller 110A-D for data communications, through amidplane (not shown), to a persistent storage resource 170A-B (alsoreferred to as a “storage resource” herein). The persistent storageresource 170A-B main include any number of storage drives 171A-F (alsoreferred to as “storage devices” herein) and any number of non-volatileRandom Access Memory (‘NVRAM’) devices (not shown).

In some implementations, the NVRAM devices of a persistent storageresource 170A-B may be configured to receive, from the storage arraycontroller 110A-D, data to be stored in the storage drives 171A-F. Insome examples, the data may originate from computing devices 164A-B. Insome examples, writing data to the NVRAM device may be carried out morequickly than directly writing data to the storage drive 171A-F. Inimplementations, the storage array controller 110A-D may be configuredto utilize the NVRAM devices as a quickly accessible buffer for datadestined to be written to the storage drives 171A-F. Latency for writerequests using NVRAM devices as a buffer may be improved relative to asystem in which a storage array controller 110A-D writes data directlyto the storage drives 171A-F. In some implementations, the NVRAM devicesmay be implemented with computer memory in the form of high bandwidth,low latency RAM. The NVRAM device is referred to as “non-volatile”because the NVRAM device may receive or include a unique power sourcethat maintains the state of the RAM after main power loss to the NVRAMdevice. Such a power source may be a battery, one or more capacitors, orthe like. In response to a power loss, the NVRAM device may beconfigured to write the contents of the RAM to a persistent storage,such as the storage drives 171A-F.

In implementations, storage drive 171A-F may refer to any deviceconfigured to record data persistently, where “persistently” or“persistent” refers as to a device's ability to maintain recorded dataafter loss of power. In some implementations, storage drive 171A-F maycorrespond to non-disk storage media. For example, the storage drive171A-F may be one or more solid-state drives (‘SSDs’), flash memorybased storage, any type of solid-state non-volatile memory, or any othertype of non-mechanical storage device. In other implementations, storagedrive 171A-F may include mechanical or spinning hard disk, such ashard-disk drives (‘HDD’).

In some implementations, the storage array controllers 110A-D may beconfigured for offloading device management responsibilities fromstorage drive 171A-F in storage array 102A-B. For example, storage arraycontrollers 110A-D may manage control information that may describe thestate of one or more memory blocks in the storage drives 171A-F. Thecontrol information may indicate, for example, that a particular memoryblock has failed and should no longer be written to, that a particularmemory block contains boot code for a storage array controller 110A-D,the number of program-erase (′P/E′) cycles that have been performed on aparticular memory block, the age of data stored in a particular memoryblock, the type of data that is stored in a particular memory block, andso forth. In some implementations, the control information may be storedwith an associated memory block as metadata. In other implementations,the control information for the storage drives 171A-F may be stored inone or more particular memory blocks of the storage drives 171A-F thatare selected by the storage array controller 110A-D. The selected memoryblocks may be tagged with an identifier indicating that the selectedmemory block contains control information. The identifier may beutilized by the storage array controllers 110A-D in conjunction withstorage drives 171A-F to quickly identify the memory blocks that containcontrol information. For example, the storage controllers 110A-D mayissue a command to locate memory blocks that contain controlinformation. It may be noted that control information may be so largethat parts of the control information may be stored in multiplelocations, that the control information may be stored in multiplelocations for purposes of redundancy, for example, or that the controlinformation may otherwise be distributed across multiple memory blocksin the storage drive 171A-F.

In implementations, storage array controllers 110A-D may offload devicemanagement responsibilities from storage drives 171A-F of storage array102A-B by retrieving, from the storage drives 171A-F, controlinformation describing the state of one or more memory blocks in thestorage drives 171A-F. Retrieving the control information from thestorage drives 171A-F may be carried out, for example, by the storagearray controller 110A-D querying the storage drives 171A-F for thelocation of control information for a particular storage drive 171A-F.The storage drives 171A-F may be configured to execute instructions thatenable the storage drive 171A-F to identify the location of the controlinformation. The instructions may be executed by a controller (notshown) associated with or otherwise located on the storage drive 171A-Fand may cause the storage drive 171A-F to scan a portion of each memoryblock to identify the memory blocks that store control information forthe storage drives 171A-F. The storage drives 171A-F may respond bysending a response message to the storage array controller 110A-D thatincludes the location of control information for the storage drive171A-F. Responsive to receiving the response message, storage arraycontrollers 110A-D may issue a request to read data stored at theaddress associated with the location of control information for thestorage drives 171A-F.

In other implementations, the storage array controllers 110A-D mayfurther offload device management responsibilities from storage drives171A-F by performing, in response to receiving the control information,a storage drive management operation. A storage drive managementoperation may include, for example, an operation that is typicallyperformed by the storage drive 171A-F (e.g., the controller (not shown)associated with a particular storage drive 171A-F). A storage drivemanagement operation may include, for example, ensuring that data is notwritten to failed memory blocks within the storage drive 171A-F,ensuring that data is written to memory blocks within the storage drive171A-F in such a way that adequate wear leveling is achieved, and soforth.

In implementations, storage array 102A-B may implement two or morestorage array controllers 110A-D. For example, storage array 102A mayinclude storage array controllers 110A and storage array controllers110B. At a given instance, a single storage array controller 110A-D(e.g., storage array controller 110A) of a storage system 100 may bedesignated with primary status (also referred to as “primary controller”herein), and other storage array controllers 110A-D (e.g., storage arraycontroller 110A) may be designated with secondary status (also referredto as “secondary controller” herein). The primary controller may haveparticular rights, such as permission to alter data in persistentstorage resource 170A-B (e.g., writing data to persistent storageresource 170A-B). At least some of the rights of the primary controllermay supersede the rights of the secondary controller. For instance, thesecondary controller may not have permission to alter data in persistentstorage resource 170A-B when the primary controller has the right. Thestatus of storage array controllers 110A-D may change. For example,storage array controller 110A may be designated with secondary status,and storage array controller 110B may be designated with primary status.

In some implementations, a primary controller, such as storage arraycontroller 110A, may serve as the primary controller for one or morestorage arrays 102A-B, and a second controller, such as storage arraycontroller 110B, may serve as the secondary controller for the one ormore storage arrays 102A-B. For example, storage array controller 110Amay be the primary controller for storage array 102A and storage array102B, and storage array controller 110B may be the secondary controllerfor storage array 102A and 102B. In some implementations, storage arraycontrollers 110C and 110D (also referred to as “storage processingmodules”) may neither have primary or secondary status. Storage arraycontrollers 110C and 110D, implemented as storage processing modules,may act as a communication interface between the primary and secondarycontrollers (e.g., storage array controllers 110A and 110B,respectively) and storage array 102B. For example, storage arraycontroller 110A of storage array 102A may send a write request, via SAN158, to storage array 102B. The write request may be received by bothstorage array controllers 110C and 110D of storage array 102B. Storagearray controllers 110C and 110D facilitate the communication, e.g., sendthe write request to the appropriate storage drive 171A-F. It may benoted that in some implementations storage processing modules may beused to increase the number of storage drives controlled by the primaryand secondary controllers.

In implementations, storage array controllers 110A-D are communicativelycoupled, via a midplane (not shown), to one or more storage drives171A-F and to one or more NVRAM devices (not shown) that are included aspart of a storage array 102A-B. The storage array controllers 110A-D maybe coupled to the midplane via one or more data communication links andthe midplane may be coupled to the storage drives 171A-F and the NVRAMdevices via one or more data communications links. The datacommunications links described herein are collectively illustrated bydata communications links 108A-D and may include a Peripheral ComponentInterconnect Express (‘PCIe’) bus, for example.

FIG. 1B illustrates an example system for data storage, in accordancewith some implementations. Storage array controller 101 illustrated inFIG. 1B may similar to the storage array controllers 110A-D describedwith respect to FIG. 1A. In one example, storage array controller 101may be similar to storage array controller 110A or storage arraycontroller 110B. Storage array controller 101 includes numerous elementsfor purposes of illustration rather than limitation. It may be notedthat storage array controller 101 may include the same, more, or fewerelements configured in the same or different manner in otherimplementations. It may be noted that elements of FIG. 1A may beincluded below to help illustrate features of storage array controller101.

Storage array controller 101 may include one or more processing devices104 and random access memory (‘RAM’) 111. Processing device 104 (orcontroller 101) represents one or more general-purpose processingdevices such as a microprocessor, central processing unit, or the like.More particularly, the processing device 104 (or controller 101) may bea complex instruction set computing (‘CISC’) microprocessor, reducedinstruction set computing (‘RISC’) microprocessor, very long instructionword (‘VLIW’) microprocessor, or a processor implementing otherinstruction sets or processors implementing a combination of instructionsets. The processing device 104 (or controller 101) may also be one ormore special-purpose processing devices such as an ASIC, an FPGA, adigital signal processor (‘DSP’), network processor, or the like.

The processing device 104 may be connected to the RAM 111 via a datacommunications link 106, which may be embodied as a high speed memorybus such as a Double-Data Rate 4 (‘DDR4’) bus. Stored in RAM 111 is anoperating system 112. In some implementations, instructions 113 arestored in RAM 111. Instructions 113 may include computer programinstructions for performing operations in in a direct-mapped flashstorage system. In one embodiment, a direct-mapped flash storage systemis one that that addresses data blocks within flash drives directly andwithout an address translation performed by the storage controllers ofthe flash drives.

In implementations, storage array controller 101 includes one or morehost bus adapters 103A-C that are coupled to the processing device 104via a data communications link 105A-C. In implementations, host busadapters 103A-C may be computer hardware that connects a host system(e.g., the storage array controller) to other network and storagearrays. In some examples, host bus adapters 103A-C may be a FibreChannel adapter that enables the storage array controller 101 to connectto a SAN, an Ethernet adapter that enables the storage array controller101 to connect to a LAN, or the like. Host bus adapters 103A-C may becoupled to the processing device 104 via a data communications link105A-C such as, for example, a PCIe bus.

In implementations, storage array controller 101 may include a host busadapter 114 that is coupled to an expander 115. The expander 115 may beused to attach a host system to a larger number of storage drives. Theexpander 115 may, for example, be a SAS expander utilized to enable thehost bus adapter 114 to attach to storage drives in an implementationwhere the host bus adapter 114 is embodied as a SAS controller.

In implementations, storage array controller 101 may include a switch116 coupled to the processing device 104 via a data communications link109. The switch 116 may be a computer hardware device that can createmultiple endpoints out of a single endpoint, thereby enabling multipledevices to share a single endpoint. The switch 116 may, for example, bea PCIe switch that is coupled to a PCIe bus (e.g., data communicationslink 109) and presents multiple PCIe connection points to the midplane.

In implementations, storage array controller 101 includes a datacommunications link 107 for coupling the storage array controller 101 toother storage array controllers. In some examples, data communicationslink 107 may be a QuickPath Interconnect (QPI) interconnect.

A traditional storage system that uses traditional flash drives mayimplement a process across the flash drives that are part of thetraditional storage system. For example, a higher level process of thestorage system may initiate and control a process across the flashdrives. However, a flash drive of the traditional storage system mayinclude its own storage controller that also performs the process. Thus,for the traditional storage system, a higher level process (e.g.,initiated by the storage system) and a lower level process (e.g.,initiated by a storage controller of the storage system) may both beperformed.

To resolve various deficiencies of a traditional storage system,operations may be performed by higher level processes and not by thelower level processes. For example, the flash storage system may includeflash drives that do not include storage controllers that provide theprocess. Thus, the operating system of the flash storage system itselfmay initiate and control the process. This may be accomplished by adirect-mapped flash storage system that addresses data blocks within theflash drives directly and without an address translation performed bythe storage controllers of the flash drives.

In implementations, storage drive 171A-F may be one or more zonedstorage devices. In some implementations, the one or more zoned storagedevices may be a shingled HDD. In implementations, the one or morestorage devices may be a flash-based SSD. In a zoned storage device, azoned namespace on the zoned storage device can be addressed by groupsof blocks that are grouped and aligned by a natural size, forming anumber of addressable zones. In implementations utilizing an SSD, thenatural size may be based on the erase block size of the SSD.

The mapping from a zone to an erase block (or to a shingled track in anHDD) may be arbitrary, dynamic, and hidden from view. The process ofopening a zone may be an operation that allows a new zone to bedynamically mapped to underlying storage of the zoned storage device,and then allows data to be written through appending writes into thezone until the zone reaches capacity. The zone can be finished at anypoint, after which further data may not be written into the zone. Whenthe data stored at the zone is no longer needed, the zone can be resetwhich effectively deletes the zone's content from the zoned storagedevice, making the physical storage held by that zone available for thesubsequent storage of data. Once a zone has been written and finished,the zoned storage device ensures that the data stored at the zone is notlost until the zone is reset. In the time between writing the data tothe zone and the resetting of the zone, the zone may be moved aroundbetween shingle tracks or erase blocks as part of maintenance operationswithin the zoned storage device, such as by copying data to keep thedata refreshed or to handle memory cell aging in an SSD.

In implementations utilizing an HDD, the resetting of the zone may allowthe shingle tracks to be allocated to a new, opened zone that may beopened at some point in the future. In implementations utilizing an SSD,the resetting of the zone may cause the associated physical eraseblock(s) of the zone to be erased and subsequently reused for thestorage of data. In some implementations, the zoned storage device mayhave a limit on the number of open zones at a point in time to reducethe amount of overhead dedicated to keeping zones open.

The operating system of the flash storage system may identify andmaintain a list of allocation units across multiple flash drives of theflash storage system. The allocation units may be entire erase blocks ormultiple erase blocks. The operating system may maintain a map oraddress range that directly maps addresses to erase blocks of the flashdrives of the flash storage system.

Direct mapping to the erase blocks of the flash drives may be used torewrite data and erase data. For example, the operations may beperformed on one or more allocation units that include a first data anda second data where the first data is to be retained and the second datais no longer being used by the flash storage system. The operatingsystem may initiate the process to write the first data to new locationswithin other allocation units and erasing the second data and markingthe allocation units as being available for use for subsequent data.Thus, the process may only be performed by the higher level operatingsystem of the flash storage system without an additional lower levelprocess being performed by controllers of the flash drives.

Advantages of the process being performed only by the operating systemof the flash storage system include increased reliability of the flashdrives of the flash storage system as unnecessary or redundant writeoperations are not being performed during the process. One possiblepoint of novelty here is the concept of initiating and controlling theprocess at the operating system of the flash storage system. Inaddition, the process can be controlled by the operating system acrossmultiple flash drives. This is contrast to the process being performedby a storage controller of a flash drive.

A storage system can consist of two storage array controllers that sharea set of drives for failover purposes, or it could consist of a singlestorage array controller that provides a storage service that utilizesmultiple drives, or it could consist of a distributed network of storagearray controllers each with some number of drives or some amount ofFlash storage where the storage array controllers in the networkcollaborate to provide a complete storage service and collaborate onvarious aspects of a storage service including storage allocation andgarbage collection.

FIG. 1C illustrates a third example system 117 for data storage inaccordance with some implementations. System 117 (also referred to as“storage system” herein) includes numerous elements for purposes ofillustration rather than limitation. It may be noted that system 117 mayinclude the same, more, or fewer elements configured in the same ordifferent manner in other implementations.

In one embodiment, system 117 includes a dual Peripheral ComponentInterconnect (‘PCI’) flash storage device 118 with separatelyaddressable fast write storage. System 117 may include a storagecontroller 119. In one embodiment, storage controller 119A-D may be aCPU, ASIC, FPGA, or any other circuitry that may implement controlstructures necessary according to the present disclosure. In oneembodiment, system 117 includes flash memory devices (e.g., includingflash memory devices 120 a-n), operatively coupled to various channelsof the storage device controller 119. Flash memory devices 120 a-n, maybe presented to the controller 119A-D as an addressable collection ofFlash pages, erase blocks, and/or control elements sufficient to allowthe storage device controller 119A-D to program and retrieve variousaspects of the Flash. In one embodiment, storage device controller119A-D may perform operations on flash memory devices 120 a-n includingstoring and retrieving data content of pages, arranging and erasing anyblocks, tracking statistics related to the use and reuse of Flash memorypages, erase blocks, and cells, tracking and predicting error codes andfaults within the Flash memory, controlling voltage levels associatedwith programming and retrieving contents of Flash cells, etc.

In one embodiment, system 117 may include RAM 121 to store separatelyaddressable fast-write data. In one embodiment, RAM 121 may be one ormore separate discrete devices. In another embodiment, RAM 121 may beintegrated into storage device controller 119A-D or multiple storagedevice controllers. The RAM 121 may be utilized for other purposes aswell, such as temporary program memory for a processing device (e.g., aCPU) in the storage device controller 119.

In one embodiment, system 117 may include a stored energy device 122,such as a rechargeable battery or a capacitor. Stored energy device 122may store energy sufficient to power the storage device controller 119,some amount of the RAM (e.g., RAM 121), and some amount of Flash memory(e.g., Flash memory 120 a-120 n) for sufficient time to write thecontents of RAM to Flash memory. In one embodiment, storage devicecontroller 119A-D may write the contents of RAM to Flash Memory if thestorage device controller detects loss of external power.

In one embodiment, system 117 includes two data communications links 123a, 123 b. In one embodiment, data communications links 123 a, 123 b maybe PCI interfaces. In another embodiment, data communications links 123a, 123 b may be based on other communications standards (e.g.,HyperTransport, InfiniBand, etc.). Data communications links 123 a, 123b may be based on non-volatile memory express (‘NVMe’) or NVMe overfabrics (‘NVMf’) specifications that allow external connection to thestorage device controller 119A-D from other components in the storagesystem 117. It should be noted that data communications links may beinterchangeably referred to herein as PCI buses for convenience.

System 117 may also include an external power source (not shown), whichmay be provided over one or both data communications links 123 a, 123 b,or which may be provided separately. An alternative embodiment includesa separate Flash memory (not shown) dedicated for use in storing thecontent of RAM 121. The storage device controller 119A-D may present alogical device over a PCI bus which may include an addressablefast-write logical device, or a distinct part of the logical addressspace of the storage device 118, which may be presented as PCI memory oras persistent storage. In one embodiment, operations to store into thedevice are directed into the RAM 121. On power failure, the storagedevice controller 119A-D may write stored content associated with theaddressable fast-write logical storage to Flash memory (e.g., Flashmemory 120 a-n) for long-term persistent storage.

In one embodiment, the logical device may include some presentation ofsome or all of the content of the Flash memory devices 120 a-n, wherethat presentation allows a storage system including a storage device 118(e.g., storage system 117) to directly address Flash memory pages anddirectly reprogram erase blocks from storage system components that areexternal to the storage device through the PCI bus. The presentation mayalso allow one or more of the external components to control andretrieve other aspects of the Flash memory including some or all of:tracking statistics related to use and reuse of Flash memory pages,erase blocks, and cells across all the Flash memory devices; trackingand predicting error codes and faults within and across the Flash memorydevices; controlling voltage levels associated with programming andretrieving contents of Flash cells; etc.

In one embodiment, the stored energy device 122 may be sufficient toensure completion of in-progress operations to the Flash memory devices120 a-120 n stored energy device 122 may power storage device controller119A-D and associated Flash memory devices (e.g., 120 a-n) for thoseoperations, as well as for the storing of fast-write RAM to Flashmemory. Stored energy device 122 may be used to store accumulatedstatistics and other parameters kept and tracked by the Flash memorydevices 120 a-n and/or the storage device controller 119. Separatecapacitors or stored energy devices (such as smaller capacitors near orembedded within the Flash memory devices themselves) may be used forsome or all of the operations described herein.

Various schemes may be used to track and optimize the life span of thestored energy component, such as adjusting voltage levels over time,partially discharging the storage energy device 122 to measurecorresponding discharge characteristics, etc. If the available energydecreases over time, the effective available capacity of the addressablefast-write storage may be decreased to ensure that it can be writtensafely based on the currently available stored energy.

FIG. 1D illustrates a third example system 124 for data storage inaccordance with some implementations. In one embodiment, system 124includes storage controllers 125 a, 125 b. In one embodiment, storagecontrollers 125 a, 125 b are operatively coupled to Dual PCI storagedevices 119 a, 119 b and 119 c, 119 d, respectively. Storage controllers125 a, 125 b may be operatively coupled (e.g., via a storage network130) to some number of host computers 127 a-n.

In one embodiment, two storage controllers (e.g., 125 a and 125 b)provide storage services, such as a SCS) block storage array, a fileserver, an object server, a database or data analytics service, etc. Thestorage controllers 125 a, 125 b may provide services through somenumber of network interfaces (e.g., 126 a-d) to host computers 127 a-noutside of the storage system 124. Storage controllers 125 a, 125 b mayprovide integrated services or an application entirely within thestorage system 124, forming a converged storage and compute system. Thestorage controllers 125 a, 125 b may utilize the fast write memorywithin or across storage devices 119 a-d to journal in progressoperations to ensure the operations are not lost on a power failure,storage controller removal, storage controller or storage systemshutdown, or some fault of one or more software or hardware componentswithin the storage system 124.

In one embodiment, controllers 125 a, 125 b operate as PCI masters toone or the other PCI buses 128 a, 128 b. In another embodiment, 128 aand 128 b may be based on other communications standards (e.g.,HyperTransport, InfiniBand, etc.). Other storage system embodiments mayoperate storage controllers 125 a, 125 b as multi-masters for both PCIbuses 128 a, 128 b. Alternately, a PCI/NVMe/NVMf switchinginfrastructure or fabric may connect multiple storage controllers. Somestorage system embodiments may allow storage devices to communicate witheach other directly rather than communicating only with storagecontrollers. In one embodiment, a storage device controller 119 a may beoperable under direction from a storage controller 125 a to synthesizeand transfer data to be stored into Flash memory devices from data thathas been stored in RAM (e.g., RAM 121 of FIG. 1C). For example, arecalculated version of RAM content may be transferred after a storagecontroller has determined that an operation has fully committed acrossthe storage system, or when fast-write memory on the device has reacheda certain used capacity, or after a certain amount of time, to ensureimprove safety of the data or to release addressable fast-write capacityfor reuse. This mechanism may be used, for example, to avoid a secondtransfer over a bus (e.g., 128 a, 128 b) from the storage controllers125 a, 125 b. In one embodiment, a recalculation may include compressingdata, attaching indexing or other metadata, combining multiple datasegments together, performing erasure code calculations, etc.

In one embodiment, under direction from a storage controller 125 a, 125b, a storage device controller 119 a, 119 b may be operable to calculateand transfer data to other storage devices from data stored in RAM(e.g., RAM 121 of FIG. 1C) without involvement of the storagecontrollers 125 a, 125 b. This operation may be used to mirror datastored in one controller 125 a to another controller 125 b, or it couldbe used to offload compression, data aggregation, and/or erasure codingcalculations and transfers to storage devices to reduce load on storagecontrollers or the storage controller interface 129 a, 129 b to the PCIbus 128 a, 128 b.

A storage device controller 119A-D may include mechanisms forimplementing high availability primitives for use by other parts of astorage system external to the Dual PCI storage device 118. For example,reservation or exclusion primitives may be provided so that, in astorage system with two storage controllers providing a highly availablestorage service, one storage controller may prevent the other storagecontroller from accessing or continuing to access the storage device.This could be used, for example, in cases where one controller detectsthat the other controller is not functioning properly or where theinterconnect between the two storage controllers may itself not befunctioning properly.

In one embodiment, a storage system for use with Dual PCI direct mappedstorage devices with separately addressable fast write storage includessystems that manage erase blocks or groups of erase blocks as allocationunits for storing data on behalf of the storage service, or for storingmetadata (e.g., indexes, logs, etc.) associated with the storageservice, or for proper management of the storage system itself. Flashpages, which may be a few kilobytes in size, may be written as dataarrives or as the storage system is to persist data for long intervalsof time (e.g., above a defined threshold of time). To commit data morequickly, or to reduce the number of writes to the Flash memory devices,the storage controllers may first write data into the separatelyaddressable fast write storage on one more storage devices.

In one embodiment, the storage controllers 125 a, 125 b may initiate theuse of erase blocks within and across storage devices (e.g., 118) inaccordance with an age and expected remaining lifespan of the storagedevices, or based on other statistics. The storage controllers 125 a,125 b may initiate garbage collection and data migration data betweenstorage devices in accordance with pages that are no longer needed aswell as to manage Flash page and erase block lifespans and to manageoverall system performance.

In one embodiment, the storage system 124 may utilize mirroring and/orerasure coding schemes as part of storing data into addressable fastwrite storage and/or as part of writing data into allocation unitsassociated with erase blocks. Erasure codes may be used across storagedevices, as well as within erase blocks or allocation units, or withinand across Flash memory devices on a single storage device, to provideredundancy against single or multiple storage device failures or toprotect against internal corruptions of Flash memory pages resultingfrom Flash memory operations or from degradation of Flash memory cells.Mirroring and erasure coding at various levels may be used to recoverfrom multiple types of failures that occur separately or in combination.

The embodiments depicted with reference to FIGS. 2A-G illustrate astorage cluster that stores user data, such as user data originatingfrom one or more user or client systems or other sources external to thestorage cluster. The storage cluster distributes user data acrossstorage nodes housed within a chassis, or across multiple chassis, usingerasure coding and redundant copies of metadata. Erasure coding refersto a method of data protection or reconstruction in which data is storedacross a set of different locations, such as disks, storage nodes orgeographic locations. Flash memory is one type of solid-state memorythat may be integrated with the embodiments, although the embodimentsmay be extended to other types of solid-state memory or other storagemedium, including non-solid state memory. Control of storage locationsand workloads are distributed across the storage locations in aclustered peer-to-peer system. Tasks such as mediating communicationsbetween the various storage nodes, detecting when a storage node hasbecome unavailable, and balancing I/Os (inputs and outputs) across thevarious storage nodes, are all handled on a distributed basis. Data islaid out or distributed across multiple storage nodes in data fragmentsor stripes that support data recovery in some embodiments. Ownership ofdata can be reassigned within a cluster, independent of input and outputpatterns. This architecture described in more detail below allows astorage node in the cluster to fail, with the system remainingoperational, since the data can be reconstructed from other storagenodes and thus remain available for input and output operations. Invarious embodiments, a storage node may be referred to as a clusternode, a blade, or a server.

The storage cluster may be contained within a chassis, i.e., anenclosure housing one or more storage nodes. A mechanism to providepower to each storage node, such as a power distribution bus, and acommunication mechanism, such as a communication bus that enablescommunication between the storage nodes are included within the chassis.The storage cluster can run as an independent system in one locationaccording to some embodiments. In one embodiment, a chassis contains atleast two instances of both the power distribution and the communicationbus which may be enabled or disabled independently. The internalcommunication bus may be an Ethernet bus, however, other technologiessuch as PCIe, InfiniBand, and others, are equally suitable. The chassisprovides a port for an external communication bus for enablingcommunication between multiple chassis, directly or through a switch,and with client systems. The external communication may use a technologysuch as Ethernet, InfiniBand, Fibre Channel, etc. In some embodiments,the external communication bus uses different communication bustechnologies for inter-chassis and client communication. If a switch isdeployed within or between chassis, the switch may act as a translationbetween multiple protocols or technologies. When multiple chassis areconnected to define a storage cluster, the storage cluster may beaccessed by a client using either proprietary interfaces or standardinterfaces such as network file system (‘NFS’), common internet filesystem (‘CIFS’), small computer system interface (‘SCSI’) or hypertexttransfer protocol (‘HTTP’). Translation from the client protocol mayoccur at the switch, chassis external communication bus or within eachstorage node. In some embodiments, multiple chassis may be coupled orconnected to each other through an aggregator switch. A portion and/orall of the coupled or connected chassis may be designated as a storagecluster. As discussed above, each chassis can have multiple blades, eachblade has a media access control (‘MAC’) address, but the storagecluster is presented to an external network as having a single clusterIP address and a single MAC address in some embodiments.

Each storage node may be one or more storage servers and each storageserver is connected to one or more non-volatile solid state memoryunits, which may be referred to as storage units or storage devices. Oneembodiment includes a single storage server in each storage node andbetween one to eight non-volatile solid state memory units, however thisone example is not meant to be limiting. The storage server may includea processor, DRAM and interfaces for the internal communication bus andpower distribution for each of the power buses. Inside the storage node,the interfaces and storage unit share a communication bus, e.g., PCIExpress, in some embodiments. The non-volatile solid state memory unitsmay directly access the internal communication bus interface through astorage node communication bus, or request the storage node to accessthe bus interface. The non-volatile solid state memory unit contains anembedded CPU, solid state storage controller, and a quantity of solidstate mass storage, e.g., between 2-32 terabytes (‘TB’) in someembodiments. An embedded volatile storage medium, such as DRAM, and anenergy reserve apparatus are included in the non-volatile solid statememory unit. In some embodiments, the energy reserve apparatus is acapacitor, super-capacitor, or battery that enables transferring asubset of DRAM contents to a stable storage medium in the case of powerloss. In some embodiments, the non-volatile solid state memory unit isconstructed with a storage class memory, such as phase change ormagnetoresistive random access memory (‘MRAM’) that substitutes for DRAMand enables a reduced power hold-up apparatus.

One of many features of the storage nodes and non-volatile solid statestorage is the ability to proactively rebuild data in a storage cluster.The storage nodes and non-volatile solid state storage can determinewhen a storage node or non-volatile solid state storage in the storagecluster is unreachable, independent of whether there is an attempt toread data involving that storage node or non-volatile solid statestorage. The storage nodes and non-volatile solid state storage thencooperate to recover and rebuild the data in at least partially newlocations. This constitutes a proactive rebuild, in that the systemrebuilds data without waiting until the data is needed for a read accessinitiated from a client system employing the storage cluster. These andfurther details of the storage memory and operation thereof arediscussed below.

FIG. 2A is a perspective view of a storage cluster 161, with multiplestorage nodes 150 and internal solid-state memory coupled to eachstorage node to provide network attached storage or storage areanetwork, in accordance with some embodiments. A network attachedstorage, storage area network, or a storage cluster, or other storagememory, could include one or more storage clusters 161, each having oneor more storage nodes 150, in a flexible and reconfigurable arrangementof both the physical components and the amount of storage memoryprovided thereby. The storage cluster 161 is designed to fit in a rack,and one or more racks can be set up and populated as desired for thestorage memory. The storage cluster 161 has a chassis 138 havingmultiple slots 142. It should be appreciated that chassis 138 may bereferred to as a housing, enclosure, or rack unit. In one embodiment,the chassis 138 has fourteen slots 142, although other numbers of slotsare readily devised. For example, some embodiments have four slots,eight slots, sixteen slots, thirty-two slots, or other suitable numberof slots. Each slot 142 can accommodate one storage node 150 in someembodiments. Chassis 138 includes flaps 148 that can be utilized tomount the chassis 138 on a rack. Fans 144 provide air circulation forcooling of the storage nodes 150 and components thereof, although othercooling components could be used, or an embodiment could be devisedwithout cooling components. A switch fabric 146 couples storage nodes150 within chassis 138 together and to a network for communication tothe memory. In an embodiment depicted in herein, the slots 142 to theleft of the switch fabric 146 and fans 144 are shown occupied by storagenodes 150, while the slots 142 to the right of the switch fabric 146 andfans 144 are empty and available for insertion of storage node 150 forillustrative purposes. This configuration is one example, and one ormore storage nodes 150 could occupy the slots 142 in various furtherarrangements. The storage node arrangements need not be sequential oradjacent in some embodiments. Storage nodes 150 are hot pluggable,meaning that a storage node 150 can be inserted into a slot 142 in thechassis 138, or removed from a slot 142, without stopping or poweringdown the system. Upon insertion or removal of storage node 150 from slot142, the system automatically reconfigures in order to recognize andadapt to the change. Reconfiguration, in some embodiments, includesrestoring redundancy and/or rebalancing data or load.

Each storage node 150 can have multiple components. In the embodimentshown here, the storage node 150 includes a printed circuit board 159populated by a CPU 156, i.e., processor, a memory 154 coupled to the CPU156, and a non-volatile solid state storage 152 coupled to the CPU 156,although other mountings and/or components could be used in furtherembodiments. The memory 154 has instructions which are executed by theCPU 156 and/or data operated on by the CPU 156. As further explainedbelow, the non-volatile solid state storage 152 includes flash or, infurther embodiments, other types of solid-state memory.

Referring to FIG. 2A, storage cluster 161 is scalable, meaning thatstorage capacity with non-uniform storage sizes is readily added, asdescribed above. One or more storage nodes 150 can be plugged into orremoved from each chassis and the storage cluster self-configures insome embodiments. Plug-in storage nodes 150, whether installed in achassis as delivered or later added, can have different sizes. Forexample, in one embodiment a storage node 150 can have any multiple of 4TB, e.g., 8 TB, 12 TB, 16 TB, 32 TB, etc. In further embodiments, astorage node 150 could have any multiple of other storage amounts orcapacities. Storage capacity of each storage node 150 is broadcast, andinfluences decisions of how to stripe the data. For maximum storageefficiency, an embodiment can self-configure as wide as possible in thestripe, subject to a predetermined requirement of continued operationwith loss of up to one, or up to two, non-volatile solid state storageunits 152 or storage nodes 150 within the chassis.

FIG. 2B is a block diagram showing a communications interconnect 173 andpower distribution bus 172 coupling multiple storage nodes 150.Referring back to FIG. 2A, the communications interconnect 173 can beincluded in or implemented with the switch fabric 146 in someembodiments. Where multiple storage clusters 161 occupy a rack, thecommunications interconnect 173 can be included in or implemented with atop of rack switch, in some embodiments. As illustrated in FIG. 2B,storage cluster 161 is enclosed within a single chassis 138. Externalport 176 is coupled to storage nodes 150 through communicationsinterconnect 173, while external port 174 is coupled directly to astorage node. External power port 178 is coupled to power distributionbus 172. Storage nodes 150 may include varying amounts and differingcapacities of non-volatile solid state storage 152 as described withreference to FIG. 2A. In addition, one or more storage nodes 150 may bea compute only storage node as illustrated in FIG. 2B. Authorities 168are implemented on the non-volatile solid state storages 152, forexample as lists or other data structures stored in memory. In someembodiments the authorities are stored within the non-volatile solidstate storage 152 and supported by software executing on a controller orother processor of the non-volatile solid state storage 152. In afurther embodiment, authorities 168 are implemented on the storage nodes150, for example as lists or other data structures stored in the memory154 and supported by software executing on the CPU 156 of the storagenode 150. Authorities 168 control how and where data is stored in thenon-volatile solid state storages 152 in some embodiments. This controlassists in determining which type of erasure coding scheme is applied tothe data, and which storage nodes 150 have which portions of the data.Each authority 168 may be assigned to a non-volatile solid state storage152. Each authority may control a range of inode numbers, segmentnumbers, or other data identifiers which are assigned to data by a filesystem, by the storage nodes 150, or by the non-volatile solid statestorage 152, in various embodiments.

Every piece of data, and every piece of metadata, has redundancy in thesystem in some embodiments. In addition, every piece of data and everypiece of metadata has an owner, which may be referred to as anauthority. If that authority is unreachable, for example through failureof a storage node, there is a plan of succession for how to find thatdata or that metadata. In various embodiments, there are redundantcopies of authorities 168. Authorities 168 have a relationship tostorage nodes 150 and non-volatile solid state storage 152 in someembodiments. Each authority 168, covering a range of data segmentnumbers or other identifiers of the data, may be assigned to a specificnon-volatile solid state storage 152. In some embodiments theauthorities 168 for all of such ranges are distributed over thenon-volatile solid state storages 152 of a storage cluster. Each storagenode 150 has a network port that provides access to the non-volatilesolid state storage(s) 152 of that storage node 150. Data can be storedin a segment, which is associated with a segment number and that segmentnumber is an indirection for a configuration of a RAID (redundant arrayof independent disks) stripe in some embodiments. The assignment and useof the authorities 168 thus establishes an indirection to data.Indirection may be referred to as the ability to reference dataindirectly, in this case via an authority 168, in accordance with someembodiments. A segment identifies a set of non-volatile solid statestorage 152 and a local identifier into the set of non-volatile solidstate storage 152 that may contain data. In some embodiments, the localidentifier is an offset into the device and may be reused sequentiallyby multiple segments. In other embodiments the local identifier isunique for a specific segment and never reused. The offsets in thenon-volatile solid state storage 152 are applied to locating data forwriting to or reading from the non-volatile solid state storage 152 (inthe form of a RAID stripe). Data is striped across multiple units ofnon-volatile solid state storage 152, which may include or be differentfrom the non-volatile solid state storage 152 having the authority 168for a particular data segment.

If there is a change in where a particular segment of data is located,e.g., during a data move or a data reconstruction, the authority 168 forthat data segment should be consulted, at that non-volatile solid statestorage 152 or storage node 150 having that authority 168. In order tolocate a particular piece of data, embodiments calculate a hash valuefor a data segment or apply an inode number or a data segment number.The output of this operation points to a non-volatile solid statestorage 152 having the authority 168 for that particular piece of data.In some embodiments there are two stages to this operation. The firststage maps an entity identifier (ID), e.g., a segment number, inodenumber, or directory number to an authority identifier. This mapping mayinclude a calculation such as a hash or a bit mask. The second stage ismapping the authority identifier to a particular non-volatile solidstate storage 152, which may be done through an explicit mapping. Theoperation is repeatable, so that when the calculation is performed, theresult of the calculation repeatably and reliably points to a particularnon-volatile solid state storage 152 having that authority 168. Theoperation may include the set of reachable storage nodes as input. Ifthe set of reachable non-volatile solid state storage units changes theoptimal set changes. In some embodiments, the persisted value is thecurrent assignment (which is always true) and the calculated value isthe target assignment the cluster will attempt to reconfigure towards.This calculation may be used to determine the optimal non-volatile solidstate storage 152 for an authority in the presence of a set ofnon-volatile solid state storage 152 that are reachable and constitutethe same cluster. The calculation also determines an ordered set of peernon-volatile solid state storage 152 that will also record the authorityto non-volatile solid state storage mapping so that the authority may bedetermined even if the assigned non-volatile solid state storage isunreachable. A duplicate or substitute authority 168 may be consulted ifa specific authority 168 is unavailable in some embodiments.

With reference to FIGS. 2A and 2B, two of the many tasks of the CPU 156on a storage node 150 are to break up write data, and reassemble readdata. When the system has determined that data is to be written, theauthority 168 for that data is located as above. When the segment ID fordata is already determined the request to write is forwarded to thenon-volatile solid state storage 152 currently determined to be the hostof the authority 168 determined from the segment. The host CPU 156 ofthe storage node 150, on which the non-volatile solid state storage 152and corresponding authority 168 reside, then breaks up or shards thedata and transmits the data out to various non-volatile solid statestorage 152. The transmitted data is written as a data stripe inaccordance with an erasure coding scheme. In some embodiments, data isrequested to be pulled, and in other embodiments, data is pushed. Inreverse, when data is read, the authority 168 for the segment IDcontaining the data is located as described above. The host CPU 156 ofthe storage node 150 on which the non-volatile solid state storage 152and corresponding authority 168 reside requests the data from thenon-volatile solid state storage and corresponding storage nodes pointedto by the authority. In some embodiments the data is read from flashstorage as a data stripe. The host CPU 156 of storage node 150 thenreassembles the read data, correcting any errors (if present) accordingto the appropriate erasure coding scheme, and forwards the reassembleddata to the network. In further embodiments, some or all of these taskscan be handled in the non-volatile solid state storage 152. In someembodiments, the segment host requests the data be sent to storage node150 by requesting pages from storage and then sending the data to thestorage node making the original request.

In embodiments, authorities 168 operate to determine how operations willproceed against particular logical elements. Each of the logicalelements may be operated on through a particular authority across aplurality of storage controllers of a storage system. The authorities168 may communicate with the plurality of storage controllers so thatthe plurality of storage controllers collectively perform operationsagainst those particular logical elements.

In embodiments, logical elements could be, for example, files,directories, object buckets, individual objects, delineated parts offiles or objects, other forms of key-value pair databases, or tables. Inembodiments, performing an operation can involve, for example, ensuringconsistency, structural integrity, and/or recoverability with otheroperations against the same logical element, reading metadata and dataassociated with that logical element, determining what data should bewritten durably into the storage system to persist any changes for theoperation, or where metadata and data can be determined to be storedacross modular storage devices attached to a plurality of the storagecontrollers in the storage system.

In some embodiments the operations are token based transactions toefficiently communicate within a distributed system. Each transactionmay be accompanied by or associated with a token, which gives permissionto execute the transaction. The authorities 168 are able to maintain apre-transaction state of the system until completion of the operation insome embodiments. The token based communication may be accomplishedwithout a global lock across the system, and also enables restart of anoperation in case of a disruption or other failure.

In some systems, for example in UNIX-style file systems, data is handledwith an index node or inode, which specifies a data structure thatrepresents an object in a file system. The object could be a file or adirectory, for example. Metadata may accompany the object, as attributessuch as permission data and a creation timestamp, among otherattributes. A segment number could be assigned to all or a portion ofsuch an object in a file system. In other systems, data segments arehandled with a segment number assigned elsewhere. For purposes ofdiscussion, the unit of distribution is an entity, and an entity can bea file, a directory or a segment. That is, entities are units of data ormetadata stored by a storage system. Entities are grouped into setscalled authorities. Each authority has an authority owner, which is astorage node that has the exclusive right to update the entities in theauthority. In other words, a storage node contains the authority, andthat the authority, in turn, contains entities.

A segment is a logical container of data in accordance with someembodiments. A segment is an address space between medium address spaceand physical flash locations, i.e., the data segment number, are in thisaddress space. Segments may also contain meta-data, which enable dataredundancy to be restored (rewritten to different flash locations ordevices) without the involvement of higher level software. In oneembodiment, an internal format of a segment contains client data andmedium mappings to determine the position of that data. Each datasegment is protected, e.g., from memory and other failures, by breakingthe segment into a number of data and parity shards, where applicable.The data and parity shards are distributed, i.e., striped, acrossnon-volatile solid state storage 152 coupled to the host CPUs 156 (SeeFIGS. 2E and 2G) in accordance with an erasure coding scheme. Usage ofthe term segments refers to the container and its place in the addressspace of segments in some embodiments. Usage of the term stripe refersto the same set of shards as a segment and includes how the shards aredistributed along with redundancy or parity information in accordancewith some embodiments.

A series of address-space transformations takes place across an entirestorage system. At the top are the directory entries (file names) whichlink to an inode. Inodes point into medium address space, where data islogically stored. Medium addresses may be mapped through a series ofindirect mediums to spread the load of large files, or implement dataservices like deduplication or snapshots. Medium addresses may be mappedthrough a series of indirect mediums to spread the load of large files,or implement data services like deduplication or snapshots. Segmentaddresses are then translated into physical flash locations. Physicalflash locations have an address range bounded by the amount of flash inthe system in accordance with some embodiments. Medium addresses andsegment addresses are logical containers, and in some embodiments use a128 bit or larger identifier so as to be practically infinite, with alikelihood of reuse calculated as longer than the expected life of thesystem. Addresses from logical containers are allocated in ahierarchical fashion in some embodiments. Initially, each non-volatilesolid state storage unit 152 may be assigned a range of address space.Within this assigned range, the non-volatile solid state storage 152 isable to allocate addresses without synchronization with othernon-volatile solid state storage 152.

Data and metadata is stored by a set of underlying storage layouts thatare optimized for varying workload patterns and storage devices. Theselayouts incorporate multiple redundancy schemes, compression formats andindex algorithms. Some of these layouts store information aboutauthorities and authority masters, while others store file metadata andfile data. The redundancy schemes include error correction codes thattolerate corrupted bits within a single storage device (such as a NANDflash chip), erasure codes that tolerate the failure of multiple storagenodes, and replication schemes that tolerate data center or regionalfailures. In some embodiments, low density parity check (‘LDPC’) code isused within a single storage unit. Reed-Solomon encoding is used withina storage cluster, and mirroring is used within a storage grid in someembodiments. Metadata may be stored using an ordered log structuredindex (such as a Log Structured Merge Tree), and large data may not bestored in a log structured layout.

In order to maintain consistency across multiple copies of an entity,the storage nodes agree implicitly on two things through calculations:(1) the authority that contains the entity, and (2) the storage nodethat contains the authority. The assignment of entities to authoritiescan be done by pseudo randomly assigning entities to authorities, bysplitting entities into ranges based upon an externally produced key, orby placing a single entity into each authority. Examples of pseudorandomschemes are linear hashing and the Replication Under Scalable Hashing(‘RUSH’) family of hashes, including Controlled Replication UnderScalable Hashing (‘CRUSH’). In some embodiments, pseudorandom assignmentis utilized only for assigning authorities to nodes because the set ofnodes can change. The set of authorities cannot change so any subjectivefunction may be applied in these embodiments. Some placement schemesautomatically place authorities on storage nodes, while other placementschemes rely on an explicit mapping of authorities to storage nodes. Insome embodiments, a pseudorandom scheme is utilized to map from eachauthority to a set of candidate authority owners. A pseudorandom datadistribution function related to CRUSH may assign authorities to storagenodes and create a list of where the authorities are assigned. Eachstorage node has a copy of the pseudorandom data distribution function,and can arrive at the same calculation for distributing, and laterfinding or locating an authority. Each of the pseudorandom schemesrequires the reachable set of storage nodes as input in some embodimentsin order to conclude the same target nodes. Once an entity has beenplaced in an authority, the entity may be stored on physical devices sothat no expected failure will lead to unexpected data loss. In someembodiments, rebalancing algorithms attempt to store the copies of allentities within an authority in the same layout and on the same set ofmachines.

Examples of expected failures include device failures, stolen machines,datacenter fires, and regional disasters, such as nuclear or geologicalevents. Different failures lead to different levels of acceptable dataloss. In some embodiments, a stolen storage node impacts neither thesecurity nor the reliability of the system, while depending on systemconfiguration, a regional event could lead to no loss of data, a fewseconds or minutes of lost updates, or even complete data loss.

In the embodiments, the placement of data for storage redundancy isindependent of the placement of authorities for data consistency. Insome embodiments, storage nodes that contain authorities do not containany persistent storage. Instead, the storage nodes are connected tonon-volatile solid state storage units that do not contain authorities.The communications interconnect between storage nodes and non-volatilesolid state storage units consists of multiple communicationtechnologies and has non-uniform performance and fault tolerancecharacteristics. In some embodiments, as mentioned above, non-volatilesolid state storage units are connected to storage nodes via PCIexpress, storage nodes are connected together within a single chassisusing Ethernet backplane, and chassis are connected together to form astorage cluster. Storage clusters are connected to clients usingEthernet or fiber channel in some embodiments. If multiple storageclusters are configured into a storage grid, the multiple storageclusters are connected using the Internet or other long-distancenetworking links, such as a “metro scale” link or private link that doesnot traverse the internet.

Authority owners have the exclusive right to modify entities, to migrateentities from one non-volatile solid state storage unit to anothernon-volatile solid state storage unit, and to add and remove copies ofentities. This allows for maintaining the redundancy of the underlyingdata. When an authority owner fails, is going to be decommissioned, oris overloaded, the authority is transferred to a new storage node.Transient failures make it non-trivial to ensure that all non-faultymachines agree upon the new authority location. The ambiguity thatarises due to transient failures can be achieved automatically by aconsensus protocol such as Paxos, hot-warm failover schemes, via manualintervention by a remote system administrator, or by a local hardwareadministrator (such as by physically removing the failed machine fromthe cluster, or pressing a button on the failed machine). In someembodiments, a consensus protocol is used, and failover is automatic. Iftoo many failures or replication events occur in too short a timeperiod, the system goes into a self-preservation mode and haltsreplication and data movement activities until an administratorintervenes in accordance with some embodiments.

As authorities are transferred between storage nodes and authorityowners update entities in their authorities, the system transfersmessages between the storage nodes and non-volatile solid state storageunits. With regard to persistent messages, messages that have differentpurposes are of different types. Depending on the type of the message,the system maintains different ordering and durability guarantees. Asthe persistent messages are being processed, the messages aretemporarily stored in multiple durable and non-durable storage hardwaretechnologies. In some embodiments, messages are stored in RAM, NVRAM andon NAND flash devices, and a variety of protocols are used in order tomake efficient use of each storage medium. Latency-sensitive clientrequests may be persisted in replicated NVRAM, and then later NAND,while background rebalancing operations are persisted directly to NAND.

Persistent messages are persistently stored prior to being transmitted.This allows the system to continue to serve client requests despitefailures and component replacement. Although many hardware componentscontain unique identifiers that are visible to system administrators,manufacturer, hardware supply chain and ongoing monitoring qualitycontrol infrastructure, applications running on top of theinfrastructure address virtualize addresses. These virtualized addressesdo not change over the lifetime of the storage system, regardless ofcomponent failures and replacements. This allows each component of thestorage system to be replaced over time without reconfiguration ordisruptions of client request processing, i.e., the system supportsnon-disruptive upgrades.

In some embodiments, the virtualized addresses are stored withsufficient redundancy. A continuous monitoring system correlateshardware and software status and the hardware identifiers. This allowsdetection and prediction of failures due to faulty components andmanufacturing details. The monitoring system also enables the proactivetransfer of authorities and entities away from impacted devices beforefailure occurs by removing the component from the critical path in someembodiments.

FIG. 2C is a multiple level block diagram, showing contents of a storagenode 150 and contents of a non-volatile solid state storage 152 of thestorage node 150. Data is communicated to and from the storage node 150by a network interface controller (‘NIC’) 202 in some embodiments. Eachstorage node 150 has a CPU 156, and one or more non-volatile solid statestorage 152, as discussed above. Moving down one level in FIG. 2C, eachnon-volatile solid state storage 152 has a relatively fast non-volatilesolid state memory, such as nonvolatile random access memory (‘NVRAM’)204, and flash memory 206. In some embodiments, NVRAM 204 may be acomponent that does not require program/erase cycles (DRAM, MRAM, PCM),and can be a memory that can support being written vastly more oftenthan the memory is read from. Moving down another level in FIG. 2C, theNVRAM 204 is implemented in one embodiment as high speed volatilememory, such as dynamic random access memory (DRAM) 216, backed up byenergy reserve 218. Energy reserve 218 provides sufficient electricalpower to keep the DRAM 216 powered long enough for contents to betransferred to the flash memory 206 in the event of power failure. Insome embodiments, energy reserve 218 is a capacitor, super-capacitor,battery, or other device, that supplies a suitable supply of energysufficient to enable the transfer of the contents of DRAM 216 to astable storage medium in the case of power loss. The flash memory 206 isimplemented as multiple flash dies 222, which may be referred to aspackages of flash dies 222 or an array of flash dies 222. It should beappreciated that the flash dies 222 could be packaged in any number ofways, with a single die per package, multiple dies per package (i.e.multichip packages), in hybrid packages, as bare dies on a printedcircuit board or other substrate, as encapsulated dies, etc. In theembodiment shown, the non-volatile solid state storage 152 has acontroller 212 or other processor, and an input output (I/O) port 210coupled to the controller 212. I/O port 210 is coupled to the CPU 156and/or the network interface controller 202 of the flash storage node150. Flash input output (I/O) port 220 is coupled to the flash dies 222,and a direct memory access unit (DMA) 214 is coupled to the controller212, the DRAM 216 and the flash dies 222. In the embodiment shown, theI/O port 210, controller 212, DMA unit 214 and flash I/O port 220 areimplemented on a programmable logic device (‘PLD’) 208, e.g., an FPGA.In this embodiment, each flash die 222 has pages, organized as sixteenkB (kilobyte) pages 224, and a register 226 through which data can bewritten to or read from the flash die 222. In further embodiments, othertypes of solid-state memory are used in place of, or in addition toflash memory illustrated within flash die 222.

Storage clusters 161, in various embodiments as disclosed herein, can becontrasted with storage arrays in general. The storage nodes 150 arepart of a collection that creates the storage cluster 161. Each storagenode 150 owns a slice of data and computing required to provide thedata. Multiple storage nodes 150 cooperate to store and retrieve thedata. Storage memory or storage devices, as used in storage arrays ingeneral, are less involved with processing and manipulating the data.Storage memory or storage devices in a storage array receive commands toread, write, or erase data. The storage memory or storage devices in astorage array are not aware of a larger system in which they areembedded, or what the data means. Storage memory or storage devices instorage arrays can include various types of storage memory, such as RAM,solid state drives, hard disk drives, etc. The storage units 152described herein have multiple interfaces active simultaneously andserving multiple purposes. In some embodiments, some of thefunctionality of a storage node 150 is shifted into a storage unit 152,transforming the storage unit 152 into a combination of storage unit 152and storage node 150. Placing computing (relative to storage data) intothe storage unit 152 places this computing closer to the data itself.The various system embodiments have a hierarchy of storage node layerswith different capabilities. By contrast, in a storage array, acontroller owns and knows everything about all of the data that thecontroller manages in a shelf or storage devices. In a storage cluster161, as described herein, multiple controllers in multiple storage units152 and/or storage nodes 150 cooperate in various ways (e.g., forerasure coding, data sharding, metadata communication and redundancy,storage capacity expansion or contraction, data recovery, and so on).

FIG. 2D shows a storage server environment, which uses embodiments ofthe storage nodes 150 and storage units 152 of FIGS. 2A-C. In thisversion, each storage unit 152 has a processor such as controller 212(see FIG. 2C), an FPGA, flash memory 206, and NVRAM 204 (which issuper-capacitor backed DRAM 216, see FIGS. 2B and 2C) on a PCIe(peripheral component interconnect express) board in a chassis 138 (seeFIG. 2A). The storage unit 152 may be implemented as a single boardcontaining storage, and may be the largest tolerable failure domaininside the chassis. In some embodiments, up to two storage units 152 mayfail and the device will continue with no data loss.

The physical storage is divided into named regions based on applicationusage in some embodiments. The NVRAM 204 is a contiguous block ofreserved memory in the storage unit 152 DRAM 216, and is backed by NANDflash. NVRAM 204 is logically divided into multiple memory regionswritten for two as spool (e.g., spool_region). Space within the NVRAM204 spools is managed by each authority 168 independently. Each deviceprovides an amount of storage space to each authority 168. Thatauthority 168 further manages lifetimes and allocations within thatspace. Examples of a spool include distributed transactions or notions.When the primary power to a storage unit 152 fails, onboardsuper-capacitors provide a short duration of power hold up. During thisholdup interval, the contents of the NVRAM 204 are flushed to flashmemory 206. On the next power-on, the contents of the NVRAM 204 arerecovered from the flash memory 206.

As for the storage unit controller, the responsibility of the logical“controller” is distributed across each of the blades containingauthorities 168. This distribution of logical control is shown in FIG.2D as a host controller 242, mid-tier controller 244 and storage unitcontroller(s) 246. Management of the control plane and the storage planeare treated independently, although parts may be physically co-locatedon the same blade. Each authority 168 effectively serves as anindependent controller. Each authority 168 provides its own data andmetadata structures, its own background workers, and maintains its ownlifecycle.

FIG. 2E is a blade 252 hardware block diagram, showing a control plane254, compute and storage planes 256, 258, and authorities 168interacting with underlying physical resources, using embodiments of thestorage nodes 150 and storage units 152 of FIGS. 2A-C in the storageserver environment of FIG. 2D. The control plane 254 is partitioned intoa number of authorities 168 which can use the compute resources in thecompute plane 256 to run on any of the blades 252. The storage plane 258is partitioned into a set of devices, each of which provides access toflash 206 and NVRAM 204 resources. In one embodiment, the compute plane256 may perform the operations of a storage array controller, asdescribed herein, on one or more devices of the storage plane 258 (e.g.,a storage array).

In the compute and storage planes 256, 258 of FIG. 2E, the authorities168 interact with the underlying physical resources (i.e., devices).From the point of view of an authority 168, its resources are stripedover all of the physical devices. From the point of view of a device, itprovides resources to all authorities 168, irrespective of where theauthorities happen to run. Each authority 168 has allocated or has beenallocated one or more partitions 260 of storage memory in the storageunits 152, e.g. partitions 260 in flash memory 206 and NVRAM 204. Eachauthority 168 uses those allocated partitions 260 that belong to it, forwriting or reading user data. Authorities can be associated withdiffering amounts of physical storage of the system. For example, oneauthority 168 could have a larger number of partitions 260 or largersized partitions 260 in one or more storage units 152 than one or moreother authorities 168.

FIG. 2F depicts elasticity software layers in blades 252 of a storagecluster, in accordance with some embodiments. In the elasticitystructure, elasticity software is symmetric, i.e., each blade's computemodule 270 runs the three identical layers of processes depicted in FIG.2F. Storage managers 274 execute read and write requests from otherblades 252 for data and metadata stored in local storage unit 152 NVRAM204 and flash 206. Authorities 168 fulfill client requests by issuingthe necessary reads and writes to the blades 252 on whose storage units152 the corresponding data or metadata resides. Endpoints 272 parseclient connection requests received from switch fabric 146 supervisorysoftware, relay the client connection requests to the authorities 168responsible for fulfillment, and relay the authorities' 168 responses toclients. The symmetric three-layer structure enables the storagesystem's high degree of concurrency. Elasticity scales out efficientlyand reliably in these embodiments. In addition, elasticity implements aunique scale-out technique that balances work evenly across allresources regardless of client access pattern, and maximizes concurrencyby eliminating much of the need for inter-blade coordination thattypically occurs with conventional distributed locking.

Still referring to FIG. 2F, authorities 168 running in the computemodules 270 of a blade 252 perform the internal operations required tofulfill client requests. One feature of elasticity is that authorities168 are stateless, i.e., they cache active data and metadata in theirown blades' 252 DRAMs for fast access, but the authorities store everyupdate in their NVRAM 204 partitions on three separate blades 252 untilthe update has been written to flash 206. All the storage system writesto NVRAM 204 are in triplicate to partitions on three separate blades252 in some embodiments. With triple-mirrored NVRAM 204 and persistentstorage protected by parity and Reed-Solomon RAID checksums, the storagesystem can survive concurrent failure of two blades 252 with no loss ofdata, metadata, or access to either.

Because authorities 168 are stateless, they can migrate between blades252. Each authority 168 has a unique identifier. NVRAM 204 and flash 206partitions are associated with authorities' 168 identifiers, not withthe blades 252 on which they are running in some. Thus, when anauthority 168 migrates, the authority 168 continues to manage the samestorage partitions from its new location. When a new blade 252 isinstalled in an embodiment of the storage cluster, the systemautomatically rebalances load by: partitioning the new blade's 252storage for use by the system's authorities 168, migrating selectedauthorities 168 to the new blade 252, starting endpoints 272 on the newblade 252 and including them in the switch fabric's 146 clientconnection distribution algorithm.

From their new locations, migrated authorities 168 persist the contentsof their NVRAM 204 partitions on flash 206, process read and writerequests from other authorities 168, and fulfill the client requeststhat endpoints 272 direct to them. Similarly, if a blade 252 fails or isremoved, the system redistributes its authorities 168 among the system'sremaining blades 252. The redistributed authorities 168 continue toperform their original functions from their new locations.

FIG. 2G depicts authorities 168 and storage resources in blades 252 of astorage cluster, in accordance with some embodiments. Each authority 168is exclusively responsible for a partition of the flash 206 and NVRAM204 on each blade 252. The authority 168 manages the content andintegrity of its partitions independently of other authorities 168.Authorities 168 compress incoming data and preserve it temporarily intheir NVRAM 204 partitions, and then consolidate, RAID-protect, andpersist the data in segments of the storage in their flash 206partitions. As the authorities 168 write data to flash 206, storagemanagers 274 perform the necessary flash translation to optimize writeperformance and maximize media longevity. In the background, authorities168 “garbage collect,” or reclaim space occupied by data that clientshave made obsolete by overwriting the data. It should be appreciatedthat since authorities' 168 partitions are disjoint, there is no needfor distributed locking to execute client and writes or to performbackground functions.

The embodiments described herein may utilize various software,communication and/or networking protocols. In addition, theconfiguration of the hardware and/or software may be adjusted toaccommodate various protocols. For example, the embodiments may utilizeActive Directory, which is a database based system that providesauthentication, directory, policy, and other services in a WINDOWS™environment. In these embodiments, LDAP (Lightweight Directory AccessProtocol) is one example application protocol for querying and modifyingitems in directory service providers such as Active Directory. In someembodiments, a network lock manager (‘NLM’) is utilized as a facilitythat works in cooperation with the Network File System (‘NFS’) toprovide a System V style of advisory file and record locking over anetwork. The Server Message Block (‘SMB’) protocol, one version of whichis also known as Common Internet File System (‘CIFS’), may be integratedwith the storage systems discussed herein. SMP operates as anapplication-layer network protocol typically used for providing sharedaccess to files, printers, and serial ports and miscellaneouscommunications between nodes on a network. SMB also provides anauthenticated inter-process communication mechanism. AMAZON™ S3 (SimpleStorage Service) is a web service offered by Amazon Web Services, andthe systems described herein may interface with Amazon S3 through webservices interfaces (REST (representational state transfer), SOAP(simple object access protocol), and BitTorrent). A RESTful API(application programming interface) breaks down a transaction to createa series of small modules. Each module addresses a particular underlyingpart of the transaction. The control or permissions provided with theseembodiments, especially for object data, may include utilization of anaccess control list (‘ACL’). The ACL is a list of permissions attachedto an object and the ACL specifies which users or system processes aregranted access to objects, as well as what operations are allowed ongiven objects. The systems may utilize Internet Protocol version 6(‘IPv6’), as well as IPv4, for the communications protocol that providesan identification and location system for computers on networks androutes traffic across the Internet. The routing of packets betweennetworked systems may include Equal-cost multi-path routing (‘ECMP’),which is a routing strategy where next-hop packet forwarding to a singledestination can occur over multiple “best paths” which tie for top placein routing metric calculations. Multi-path routing can be used inconjunction with most routing protocols, because it is a per-hopdecision limited to a single router. The software may supportMulti-tenancy, which is an architecture in which a single instance of asoftware application serves multiple customers. Each customer may bereferred to as a tenant. Tenants may be given the ability to customizesome parts of the application, but may not customize the application'scode, in some embodiments. The embodiments may maintain audit logs. Anaudit log is a document that records an event in a computing system. Inaddition to documenting what resources were accessed, audit log entriestypically include destination and source addresses, a timestamp, anduser login information for compliance with various regulations. Theembodiments may support various key management policies, such asencryption key rotation. In addition, the system may support dynamicroot passwords or some variation dynamically changing passwords.

FIG. 3A sets forth a diagram of a storage system 306 that is coupled fordata communications with a cloud services provider 302 in accordancewith some embodiments of the present disclosure. Although depicted inless detail, the storage system 306 depicted in FIG. 3A may be similarto the storage systems described above with reference to FIGS. 1A-1D andFIGS. 2A-2G. In some embodiments, the storage system 306 depicted inFIG. 3A may be embodied as a storage system that includes imbalancedactive/active controllers, as a storage system that includes balancedactive/active controllers, as a storage system that includesactive/active controllers where less than all of each controller'sresources are utilized such that each controller has reserve resourcesthat may be used to support failover, as a storage system that includesfully active/active controllers, as a storage system that includesdataset-segregated controllers, as a storage system that includesdual-layer architectures with front-end controllers and back-endintegrated storage controllers, as a storage system that includesscale-out clusters of dual-controller arrays, as well as combinations ofsuch embodiments.

In the example depicted in FIG. 3A, the storage system 306 is coupled tothe cloud services provider 302 via a data communications link 304. Thedata communications link 304 may be embodied as a dedicated datacommunications link, as a data communications pathway that is providedthrough the use of one or data communications networks such as a widearea network (‘WAN’) or LAN, or as some other mechanism capable oftransporting digital information between the storage system 306 and thecloud services provider 302. Such a data communications link 304 may befully wired, fully wireless, or some aggregation of wired and wirelessdata communications pathways. In such an example, digital informationmay be exchanged between the storage system 306 and the cloud servicesprovider 302 via the data communications link 304 using one or more datacommunications protocols. For example, digital information may beexchanged between the storage system 306 and the cloud services provider302 via the data communications link 304 using the handheld devicetransfer protocol (‘HDTP’), hypertext transfer protocol (‘HTTP’),internet protocol (‘IP’), real-time transfer protocol (‘RTP’),transmission control protocol (‘TCP’), user datagram protocol (‘UDP’),wireless application protocol (‘WAP’), or other protocol.

The cloud services provider 302 depicted in FIG. 3A may be embodied, forexample, as a system and computing environment that provides a vastarray of services to users of the cloud services provider 302 throughthe sharing of computing resources via the data communications link 304.The cloud services provider 302 may provide on-demand access to a sharedpool of configurable computing resources such as computer networks,servers, storage, applications and services, and so on. The shared poolof configurable resources may be rapidly provisioned and released to auser of the cloud services provider 302 with minimal management effort.Generally, the user of the cloud services provider 302 is unaware of theexact computing resources utilized by the cloud services provider 302 toprovide the services. Although in many cases such a cloud servicesprovider 302 may be accessible via the Internet, readers of skill in theart will recognize that any system that abstracts the use of sharedresources to provide services to a user through any data communicationslink may be considered a cloud services provider 302.

In the example depicted in FIG. 3A, the cloud services provider 302 maybe configured to provide a variety of services to the storage system 306and users of the storage system 306 through the implementation ofvarious service models. For example, the cloud services provider 302 maybe configured to provide services through the implementation of aninfrastructure as a service (‘IaaS’) service model, through theimplementation of a platform as a service (‘PaaS’) service model,through the implementation of a software as a service (‘SaaS’) servicemodel, through the implementation of an authentication as a service(‘AaaS’) service model, through the implementation of a storage as aservice model where the cloud services provider 302 offers access to itsstorage infrastructure for use by the storage system 306 and users ofthe storage system 306, and so on. Readers will appreciate that thecloud services provider 302 may be configured to provide additionalservices to the storage system 306 and users of the storage system 306through the implementation of additional service models, as the servicemodels described above are included only for explanatory purposes and inno way represent a limitation of the services that may be offered by thecloud services provider 302 or a limitation as to the service modelsthat may be implemented by the cloud services provider 302.

In the example depicted in FIG. 3A, the cloud services provider 302 maybe embodied, for example, as a private cloud, as a public cloud, or as acombination of a private cloud and public cloud. In an embodiment inwhich the cloud services provider 302 is embodied as a private cloud,the cloud services provider 302 may be dedicated to providing servicesto a single organization rather than providing services to multipleorganizations. In an embodiment where the cloud services provider 302 isembodied as a public cloud, the cloud services provider 302 may provideservices to multiple organizations. In still alternative embodiments,the cloud services provider 302 may be embodied as a mix of a privateand public cloud services with a hybrid cloud deployment.

Although not explicitly depicted in FIG. 3A, readers will appreciatethat a vast amount of additional hardware components and additionalsoftware components may be necessary to facilitate the delivery of cloudservices to the storage system 306 and users of the storage system 306.For example, the storage system 306 may be coupled to (or even include)a cloud storage gateway. Such a cloud storage gateway may be embodied,for example, as hardware-based or software-based appliance that islocated on premise with the storage system 306. Such a cloud storagegateway may operate as a bridge between local applications that areexecuting on the storage array 306 and remote, cloud-based storage thatis utilized by the storage array 306. Through the use of a cloud storagegateway, organizations may move primary iSCSI or NAS to the cloudservices provider 302, thereby enabling the organization to save spaceon their on-premises storage systems. Such a cloud storage gateway maybe configured to emulate a disk array, a block-based device, a fileserver, or other storage system that can translate the SCSI commands,file server commands, or other appropriate command into REST-spaceprotocols that facilitate communications with the cloud servicesprovider 302.

In order to enable the storage system 306 and users of the storagesystem 306 to make use of the services provided by the cloud servicesprovider 302, a cloud migration process may take place during whichdata, applications, or other elements from an organization's localsystems (or even from another cloud environment) are moved to the cloudservices provider 302. In order to successfully migrate data,applications, or other elements to the cloud services provider's 302environment, middleware such as a cloud migration tool may be utilizedto bridge gaps between the cloud services provider's 302 environment andan organization's environment. Such cloud migration tools may also beconfigured to address potentially high network costs and long transfertimes associated with migrating large volumes of data to the cloudservices provider 302, as well as addressing security concernsassociated with sensitive data to the cloud services provider 302 overdata communications networks. In order to further enable the storagesystem 306 and users of the storage system 306 to make use of theservices provided by the cloud services provider 302, a cloudorchestrator may also be used to arrange and coordinate automated tasksin pursuit of creating a consolidated process or workflow. Such a cloudorchestrator may perform tasks such as configuring various components,whether those components are cloud components or on-premises components,as well as managing the interconnections between such components. Thecloud orchestrator can simplify the inter-component communication andconnections to ensure that links are correctly configured andmaintained.

In the example depicted in FIG. 3A, and as described briefly above, thecloud services provider 302 may be configured to provide services to thestorage system 306 and users of the storage system 306 through the usageof a SaaS service model, eliminating the need to install and run theapplication on local computers, which may simplify maintenance andsupport of the application. Such applications may take many forms inaccordance with various embodiments of the present disclosure. Forexample, the cloud services provider 302 may be configured to provideaccess to data analytics applications to the storage system 306 andusers of the storage system 306. Such data analytics applications may beconfigured, for example, to receive vast amounts of telemetry dataphoned home by the storage system 306. Such telemetry data may describevarious operating characteristics of the storage system 306 and may beanalyzed for a vast array of purposes including, for example, todetermine the health of the storage system 306, to identify workloadsthat are executing on the storage system 306, to predict when thestorage system 306 will run out of various resources, to recommendconfiguration changes, hardware or software upgrades, workflowmigrations, or other actions that may improve the operation of thestorage system 306.

The cloud services provider 302 may also be configured to provide accessto virtualized computing environments to the storage system 306 andusers of the storage system 306. Such virtualized computing environmentsmay be embodied, for example, as a virtual machine or other virtualizedcomputer hardware platforms, virtual storage devices, virtualizedcomputer network resources, and so on. Examples of such virtualizedenvironments can include virtual machines that are created to emulate anactual computer, virtualized desktop environments that separate alogical desktop from a physical machine, virtualized file systems thatallow uniform access to different types of concrete file systems, andmany others.

Although the example depicted in FIG. 3A illustrates the storage system306 being coupled for data communications with the cloud servicesprovider 302, in other embodiments the storage system 306 may be part ofa hybrid cloud deployment in which private cloud elements (e.g., privatecloud services, on-premises infrastructure, and so on) and public cloudelements (e.g., public cloud services, infrastructure, and so on thatmay be provided by one or more cloud services providers) are combined toform a single solution, with orchestration among the various platforms.Such a hybrid cloud deployment may leverage hybrid cloud managementsoftware such as, for example, Azure™ Arc from Microsoft™, thatcentralize the management of the hybrid cloud deployment to anyinfrastructure and enable the deployment of services anywhere. In suchan example, the hybrid cloud management software may be configured tocreate, update, and delete resources (both physical and virtual) thatform the hybrid cloud deployment, to allocate compute and storage tospecific workloads, to monitor workloads and resources for performance,policy compliance, updates and patches, security status, or to perform avariety of other tasks.

Readers will appreciate that by pairing the storage systems describedherein with one or more cloud services providers, various offerings maybe enabled. For example, disaster recovery as a service (‘DRaaS’) may beprovided where cloud resources are utilized to protect applications anddata from disruption caused by disaster, including in embodiments wherethe storage systems may serve as the primary data store. In suchembodiments, a total system backup may be taken that allows for businesscontinuity in the event of system failure. In such embodiments, clouddata backup techniques (by themselves or as part of a larger DRaaSsolution) may also be integrated into an overall solution that includesthe storage systems and cloud services providers described herein.

The storage systems described herein, as well as the cloud servicesproviders, may be utilized to provide a wide array of security features.For example, the storage systems may encrypt data at rest (and data maybe sent to and from the storage systems encrypted) and may make use ofKey Management-as-a-Service (‘KMaaS’) to manage encryption keys, keysfor locking and unlocking storage devices, and so on. Likewise, clouddata security gateways or similar mechanisms may be utilized to ensurethat data stored within the storage systems does not improperly end upbeing stored in the cloud as part of a cloud data backup operation.Furthermore, microsegmentation or identity-based-segmentation may beutilized in a data center that includes the storage systems or withinthe cloud services provider, to create secure zones in data centers andcloud deployments that enables the isolation of workloads from oneanother.

For further explanation, FIG. 3B sets forth a diagram of a storagesystem 306 in accordance with some embodiments of the presentdisclosure. Although depicted in less detail, the storage system 306depicted in FIG. 3B may be similar to the storage systems describedabove with reference to FIGS. 1A-1D and FIGS. 2A-2G as the storagesystem may include many of the components described above.

The storage system 306 depicted in FIG. 3B may include a vast amount ofstorage resources 308, which may be embodied in many forms. For example,the storage resources 308 can include nano-RAM or another form ofnonvolatile random access memory that utilizes carbon nanotubesdeposited on a substrate, 3D crosspoint non-volatile memory, flashmemory including single-level cell (‘SLC’) NAND flash, multi-level cell(‘MLC’) NAND flash, triple-level cell (‘TLC’) NAND flash, quad-levelcell (‘QLC’) NAND flash, or others. Likewise, the storage resources 308may include non-volatile magnetoresistive random-access memory (‘MRAM’),including spin transfer torque (‘STT’) MRAM. The example storageresources 308 may alternatively include non-volatile phase-change memory(‘PCM’), quantum memory that allows for the storage and retrieval ofphotonic quantum information, resistive random-access memory (‘ReRAM’),storage class memory (‘SCM’), or other form of storage resources,including any combination of resources described herein. Readers willappreciate that other forms of computer memories and storage devices maybe utilized by the storage systems described above, including DRAM,SRAM, EEPROM, universal memory, and many others. The storage resources308 depicted in FIG. 3A may be embodied in a variety of form factors,including but not limited to, dual in-line memory modules (‘DIMMs’),non-volatile dual in-line memory modules (‘NVDIMMs’), M.2, U.2, andothers.

The storage resources 308 depicted in FIG. 3B may include various formsof SCM. SCM may effectively treat fast, non-volatile memory (e.g., NANDflash) as an extension of DRAM such that an entire dataset may betreated as an in-memory dataset that resides entirely in DRAM. SCM mayinclude non-volatile media such as, for example, NAND flash. Such NANDflash may be accessed utilizing NVMe that can use the PCIe bus as itstransport, providing for relatively low access latencies compared toolder protocols. In fact, the network protocols used for SSDs inall-flash arrays can include NVMe using Ethernet (ROCE, NVME TCP), FibreChannel (NVMe FC), InfiniBand (iWARP), and others that make it possibleto treat fast, non-volatile memory as an extension of DRAM. In view ofthe fact that DRAM is often byte-addressable and fast, non-volatilememory such as NAND flash is block-addressable, a controllersoftware/hardware stack may be needed to convert the block data to thebytes that are stored in the media. Examples of media and software thatmay be used as SCM can include, for example, 3D XPoint, Intel MemoryDrive Technology, Samsung's Z-SSD, and others.

The storage resources 308 depicted in FIG. 3B may also include racetrackmemory (also referred to as domain-wall memory). Such racetrack memorymay be embodied as a form of non-volatile, solid-state memory thatrelies on the intrinsic strength and orientation of the magnetic fieldcreated by an electron as it spins in addition to its electronic charge,in solid-state devices. Through the use of spin-coherent electriccurrent to move magnetic domains along a nanoscopic permalloy wire, thedomains may pass by magnetic read/write heads positioned near the wireas current is passed through the wire, which alter the domains to recordpatterns of bits. In order to create a racetrack memory device, manysuch wires and read/write elements may be packaged together.

The example storage system 306 depicted in FIG. 3B may implement avariety of storage architectures. For example, storage systems inaccordance with some embodiments of the present disclosure may utilizeblock storage where data is stored in blocks, and each block essentiallyacts as an individual hard drive. Storage systems in accordance withsome embodiments of the present disclosure may utilize object storage,where data is managed as objects. Each object may include the dataitself, a variable amount of metadata, and a globally unique identifier,where object storage can be implemented at multiple levels (e.g., devicelevel, system level, interface level). Storage systems in accordancewith some embodiments of the present disclosure utilize file storage inwhich data is stored in a hierarchical structure. Such data may be savedin files and folders, and presented to both the system storing it andthe system retrieving it in the same format.

The example storage system 306 depicted in FIG. 3B may be embodied as astorage system in which additional storage resources can be addedthrough the use of a scale-up model, additional storage resources can beadded through the use of a scale-out model, or through some combinationthereof. In a scale-up model, additional storage may be added by addingadditional storage devices. In a scale-out model, however, additionalstorage nodes may be added to a cluster of storage nodes, where suchstorage nodes can include additional processing resources, additionalnetworking resources, and so on.

The example storage system 306 depicted in FIG. 3B may leverage thestorage resources described above in a variety of different ways. Forexample, some portion of the storage resources may be utilized to serveas a write cache where data is initially written to storage resourceswith relatively fast write latencies, relatively high write bandwidth,or similar characteristics. In such an example, data that is written tothe storage resources that serve as a write cache may later be writtento other storage resources that may be characterized by slower writelatencies, lower write bandwidth, or similar characteristics than thestorage resources that are utilized to serve as a write cache. In asimilar manner, storage resources within the storage system may beutilized as a read cache, where the read cache is populated inaccordance with a set of predetermined rules or heuristics. In otherembodiments, tiering may be achieved within the storage systems byplacing data within the storage system in accordance with one or morepolicies such that, for example, data that is accessed frequently isstored in faster storage tiers while data that is accessed infrequentlyis stored in slower storage tiers.

The storage system 306 depicted in FIG. 3B also includes communicationsresources 310 that may be useful in facilitating data communicationsbetween components within the storage system 306, as well as datacommunications between the storage system 306 and computing devices thatare outside of the storage system 306, including embodiments where thoseresources are separated by a relatively vast expanse. The communicationsresources 310 may be configured to utilize a variety of differentprotocols and data communication fabrics to facilitate datacommunications between components within the storage systems as well ascomputing devices that are outside of the storage system. For example,the communications resources 310 can include fibre channel (‘FC’)technologies such as FC fabrics and FC protocols that can transport SCSIcommands over FC network, FC over ethernet (‘FCoE’) technologies throughwhich FC frames are encapsulated and transmitted over Ethernet networks,InfiniBand (‘IB’) technologies in which a switched fabric topology isutilized to facilitate transmissions between channel adapters, NVMExpress (‘NVMe’) technologies and NVMe over fabrics (‘NVMeoF’)technologies through which non-volatile storage media attached via a PCIexpress (‘PCIe’) bus may be accessed, and others. In fact, the storagesystems described above may, directly or indirectly, make use ofneutrino communication technologies and devices through whichinformation (including binary information) is transmitted using a beamof neutrinos.

The communications resources 310 can also include mechanisms foraccessing storage resources 308 within the storage system 306 utilizingserial attached SCSI (‘SAS’), serial ATA (‘SATA’) bus interfaces forconnecting storage resources 308 within the storage system 306 to hostbus adapters within the storage system 306, internet small computersystems interface (‘iSCSI’) technologies to provide block-level accessto storage resources 308 within the storage system 306, and othercommunications resources that that may be useful in facilitating datacommunications between components within the storage system 306, as wellas data communications between the storage system 306 and computingdevices that are outside of the storage system 306.

The storage system 306 depicted in FIG. 3B also includes processingresources 312 that may be useful in useful in executing computer programinstructions and performing other computational tasks within the storagesystem 306. The processing resources 312 may include one or more ASICsthat are customized for some particular purpose as well as one or moreCPUs. The processing resources 312 may also include one or more DSPs,one or more FPGAs, one or more systems on a chip (‘SoCs’), or other formof processing resources 312. The storage system 306 may utilize thestorage resources 312 to perform a variety of tasks including, but notlimited to, supporting the execution of software resources 314 that willbe described in greater detail below.

The storage system 306 depicted in FIG. 3B also includes softwareresources 314 that, when executed by processing resources 312 within thestorage system 306, may perform a vast array of tasks. The softwareresources 314 may include, for example, one or more modules of computerprogram instructions that when executed by processing resources 312within the storage system 306 are useful in carrying out various dataprotection techniques to preserve the integrity of data that is storedwithin the storage systems. Readers will appreciate that such dataprotection techniques may be carried out, for example, by systemsoftware executing on computer hardware within the storage system, by acloud services provider, or in other ways. Such data protectiontechniques can include, for example, data archiving techniques thatcause data that is no longer actively used to be moved to a separatestorage device or separate storage system for long-term retention, databackup techniques through which data stored in the storage system may becopied and stored in a distinct location to avoid data loss in the eventof equipment failure or some other form of catastrophe with the storagesystem, data replication techniques through which data stored in thestorage system is replicated to another storage system such that thedata may be accessible via multiple storage systems, data snapshottingtechniques through which the state of data within the storage system iscaptured at various points in time, data and database cloning techniquesthrough which duplicate copies of data and databases may be created, andother data protection techniques.

The software resources 314 may also include software that is useful inimplementing software-defined storage (‘SDS’). In such an example, thesoftware resources 314 may include one or more modules of computerprogram instructions that, when executed, are useful in policy-basedprovisioning and management of data storage that is independent of theunderlying hardware. Such software resources 314 may be useful inimplementing storage virtualization to separate the storage hardwarefrom the software that manages the storage hardware.

The software resources 314 may also include software that is useful infacilitating and optimizing I/O operations that are directed to thestorage resources 308 in the storage system 306. For example, thesoftware resources 314 may include software modules that perform carryout various data reduction techniques such as, for example, datacompression, data deduplication, and others. The software resources 314may include software modules that intelligently group together I/Ooperations to facilitate better usage of the underlying storage resource308, software modules that perform data migration operations to migratefrom within a storage system, as well as software modules that performother functions. Such software resources 314 may be embodied as one ormore software containers or in many other ways.

For further explanation, FIG. 3C sets forth an example of a cloud-basedstorage system 318 in accordance with some embodiments of the presentdisclosure. In the example depicted in FIG. 3C, the cloud-based storagesystem 318 is created entirely in a cloud computing environment 316 suchas, for example, Amazon Web Services (‘AWS’), Microsoft Azure, GoogleCloud Platform, IBM Cloud, Oracle Cloud, and others. The cloud-basedstorage system 318 may be used to provide services similar to theservices that may be provided by the storage systems described above.For example, the cloud-based storage system 318 may be used to provideblock storage services to users of the cloud-based storage system 318,the cloud-based storage system 318 may be used to provide storageservices to users of the cloud-based storage system 318 through the useof solid-state storage, and so on.

The cloud-based storage system 318 depicted in FIG. 3C includes twocloud computing instances 320, 322 that each are used to support theexecution of a storage controller application 324, 326. The cloudcomputing instances 320, 322 may be embodied, for example, as instancesof cloud computing resources (e.g., virtual machines) that may beprovided by the cloud computing environment 316 to support the executionof software applications such as the storage controller application 324,326. In one embodiment, the cloud computing instances 320, 322 may beembodied as Amazon Elastic Compute Cloud (‘EC2’) instances. In such anexample, an Amazon Machine Image (‘AMI’) that includes the storagecontroller application 324, 326 may be booted to create and configure avirtual machine that may execute the storage controller application 324,326.

In the example method depicted in FIG. 3C, the storage controllerapplication 324, 326 may be embodied as a module of computer programinstructions that, when executed, carries out various storage tasks. Forexample, the storage controller application 324, 326 may be embodied asa module of computer program instructions that, when executed, carriesout the same tasks as the controllers 110A, 1108 in FIG. 1A describedabove such as writing data received from the users of the cloud-basedstorage system 318 to the cloud-based storage system 318, erasing datafrom the cloud-based storage system 318, retrieving data from thecloud-based storage system 318 and providing such data to users of thecloud-based storage system 318, monitoring and reporting of diskutilization and performance, performing redundancy operations, such asRAID or RAID-like data redundancy operations, compressing data,encrypting data, deduplicating data, and so forth. Readers willappreciate that because there are two cloud computing instances 320, 322that each include the storage controller application 324, 326, in someembodiments one cloud computing instance 320 may operate as the primarycontroller as described above while the other cloud computing instance322 may operate as the secondary controller as described above. Readerswill appreciate that the storage controller application 324, 326depicted in FIG. 3C may include identical source code that is executedwithin different cloud computing instances 320, 322.

Consider an example in which the cloud computing environment 316 isembodied as AWS and the cloud computing instances are embodied as EC2instances. In such an example, the cloud computing instance 320 thatoperates as the primary controller may be deployed on one of theinstance types that has a relatively large amount of memory andprocessing power while the cloud computing instance 322 that operates asthe secondary controller may be deployed on one of the instance typesthat has a relatively small amount of memory and processing power. Insuch an example, upon the occurrence of a failover event where the rolesof primary and secondary are switched, a double failover may actually becarried out such that: 1) a first failover event where the cloudcomputing instance 322 that formerly operated as the secondarycontroller begins to operate as the primary controller, and 2) a thirdcloud computing instance (not shown) that is of an instance type thathas a relatively large amount of memory and processing power is spun upwith a copy of the storage controller application, where the third cloudcomputing instance begins operating as the primary controller while thecloud computing instance 322 that originally operated as the secondarycontroller begins operating as the secondary controller again. In suchan example, the cloud computing instance 320 that formerly operated asthe primary controller may be terminated. Readers will appreciate thatin alternative embodiments, the cloud computing instance 320 that isoperating as the secondary controller after the failover event maycontinue to operate as the secondary controller and the cloud computinginstance 322 that operated as the primary controller after theoccurrence of the failover event may be terminated once the primary rolehas been assumed by the third cloud computing instance (not shown).

Readers will appreciate that while the embodiments described aboverelate to embodiments where one cloud computing instance 320 operates asthe primary controller and the second cloud computing instance 322operates as the secondary controller, other embodiments are within thescope of the present disclosure. For example, each cloud computinginstance 320, 322 may operate as a primary controller for some portionof the address space supported by the cloud-based storage system 318,each cloud computing instance 320, 322 may operate as a primarycontroller where the servicing of I/O operations directed to thecloud-based storage system 318 are divided in some other way, and so on.In fact, in other embodiments where costs savings may be prioritizedover performance demands, only a single cloud computing instance mayexist that contains the storage controller application.

The cloud-based storage system 318 depicted in FIG. 3C includes cloudcomputing instances 340 a, 340 b, 340 n with local storage 330, 334,338. The cloud computing instances 340 a, 340 b, 340 n depicted in FIG.3C may be embodied, for example, as instances of cloud computingresources that may be provided by the cloud computing environment 316 tosupport the execution of software applications. The cloud computinginstances 340 a, 340 b, 340 n of FIG. 3C may differ from the cloudcomputing instances 320, 322 described above as the cloud computinginstances 340 a, 340 b, 340 n of FIG. 3C have local storage 330, 334,338 resources whereas the cloud computing instances 320, 322 thatsupport the execution of the storage controller application 324, 326need not have local storage resources. The cloud computing instances 340a, 340 b, 340 n with local storage 330, 334, 338 may be embodied, forexample, as EC2 M5 instances that include one or more SSDs, as EC2 R5instances that include one or more SSDs, as EC2 I3 instances thatinclude one or more SSDs, and so on. In some embodiments, the localstorage 330, 334, 338 must be embodied as solid-state storage (e.g.,SSDs) rather than storage that makes use of hard disk drives.

In the example depicted in FIG. 3C, each of the cloud computinginstances 340 a, 340 b, 340 n with local storage 330, 334, 338 caninclude a software daemon 328, 332, 336 that, when executed by a cloudcomputing instance 340 a, 340 b, 340 n can present itself to the storagecontroller applications 324, 326 as if the cloud computing instance 340a, 340 b, 340 n were a physical storage device (e.g., one or more SSDs).In such an example, the software daemon 328, 332, 336 may includecomputer program instructions similar to those that would normally becontained on a storage device such that the storage controllerapplications 324, 326 can send and receive the same commands that astorage controller would send to storage devices. In such a way, thestorage controller applications 324, 326 may include code that isidentical to (or substantially identical to) the code that would beexecuted by the controllers in the storage systems described above. Inthese and similar embodiments, communications between the storagecontroller applications 324, 326 and the cloud computing instances 340a, 340 b, 340 n with local storage 330, 334, 338 may utilize iSCSI, NVMeover TCP, messaging, a custom protocol, or in some other mechanism.

In the example depicted in FIG. 3C, each of the cloud computinginstances 340 a, 340 b, 340 n with local storage 330, 334, 338 may alsobe coupled to block-storage 342, 344, 346 that is offered by the cloudcomputing environment 316. The block-storage 342, 344, 346 that isoffered by the cloud computing environment 316 may be embodied, forexample, as Amazon Elastic Block Store (‘EBS’) volumes. For example, afirst EBS volume may be coupled to a first cloud computing instance 340a, a second EBS volume may be coupled to a second cloud computinginstance 340 b, and a third EBS volume may be coupled to a third cloudcomputing instance 340 n. In such an example, the block-storage 342,344, 346 that is offered by the cloud computing environment 316 may beutilized in a manner that is similar to how the NVRAM devices describedabove are utilized, as the software daemon 328, 332, 336 (or some othermodule) that is executing within a particular cloud comping instance 340a, 340 b, 340 n may, upon receiving a request to write data, initiate awrite of the data to its attached EBS volume as well as a write of thedata to its local storage 330, 334, 338 resources. In some alternativeembodiments, data may only be written to the local storage 330, 334, 338resources within a particular cloud comping instance 340 a, 340 b, 340n. In an alternative embodiment, rather than using the block-storage342, 344, 346 that is offered by the cloud computing environment 316 asNVRAM, actual RAM on each of the cloud computing instances 340 a, 340 b,340 n with local storage 330, 334, 338 may be used as NVRAM, therebydecreasing network utilization costs that would be associated with usingan EBS volume as the NVRAM.

In the example depicted in FIG. 3C, the cloud computing instances 340 a,340 b, 340 n with local storage 330, 334, 338 may be utilized, by cloudcomputing instances 320, 322 that support the execution of the storagecontroller application 324, 326 to service I/O operations that aredirected to the cloud-based storage system 318. Consider an example inwhich a first cloud computing instance 320 that is executing the storagecontroller application 324 is operating as the primary controller. Insuch an example, the first cloud computing instance 320 that isexecuting the storage controller application 324 may receive (directlyor indirectly via the secondary controller) requests to write data tothe cloud-based storage system 318 from users of the cloud-based storagesystem 318. In such an example, the first cloud computing instance 320that is executing the storage controller application 324 may performvarious tasks such as, for example, deduplicating the data contained inthe request, compressing the data contained in the request, determiningwhere to the write the data contained in the request, and so on, beforeultimately sending a request to write a deduplicated, encrypted, orotherwise possibly updated version of the data to one or more of thecloud computing instances 340 a, 340 b, 340 n with local storage 330,334, 338. Either cloud computing instance 320, 322, in some embodiments,may receive a request to read data from the cloud-based storage system318 and may ultimately send a request to read data to one or more of thecloud computing instances 340 a, 340 b, 340 n with local storage 330,334, 338.

Readers will appreciate that when a request to write data is received bya particular cloud computing instance 340 a, 340 b, 340 n with localstorage 330, 334, 338, the software daemon 328, 332, 336 or some othermodule of computer program instructions that is executing on theparticular cloud computing instance 340 a, 340 b, 340 n may beconfigured to not only write the data to its own local storage 330, 334,338 resources and any appropriate block-storage 342, 344, 346 that areoffered by the cloud computing environment 316, but the software daemon328, 332, 336 or some other module of computer program instructions thatis executing on the particular cloud computing instance 340 a, 340 b,340 n may also be configured to write the data to cloud-based objectstorage 348 that is attached to the particular cloud computing instance340 a, 340 b, 340 n. The cloud-based object storage 348 that is attachedto the particular cloud computing instance 340 a, 340 b, 340 n may beembodied, for example, as Amazon Simple Storage Service (‘S3’) storagethat is accessible by the particular cloud computing instance 340 a, 340b, 340 n. In other embodiments, the cloud computing instances 320, 322that each include the storage controller application 324, 326 mayinitiate the storage of the data in the local storage 330, 334, 338 ofthe cloud computing instances 340 a, 340 b, 340 n and the cloud-basedobject storage 348.

Readers will appreciate that, as described above, the cloud-basedstorage system 318 may be used to provide block storage services tousers of the cloud-based storage system 318. While the local storage330, 334, 338 resources and the block-storage 342, 344, 346 resourcesthat are utilized by the cloud computing instances 340 a, 340 b, 340 nmay support block-level access, the cloud-based object storage 348 thatis attached to the particular cloud computing instance 340 a, 340 b, 340n supports only object-based access. In order to address this, thesoftware daemon 328, 332, 336 or some other module of computer programinstructions that is executing on the particular cloud computinginstance 340 a, 340 b, 340 n may be configured to take blocks of data,package those blocks into objects, and write the objects to thecloud-based object storage 348 that is attached to the particular cloudcomputing instance 340 a, 340 b, 340 n.

Consider an example in which data is written to the local storage 330,334, 338 resources and the block-storage 342, 344, 346 resources thatare utilized by the cloud computing instances 340 a, 340 b, 340 n in 1MB blocks. In such an example, assume that a user of the cloud-basedstorage system 318 issues a request to write data that, after beingcompressed and deduplicated by the storage controller application 324,326 results in the need to write 5 MB of data. In such an example,writing the data to the local storage 330, 334, 338 resources and theblock-storage 342, 344, 346 resources that are utilized by the cloudcomputing instances 340 a, 340 b, 340 n is relatively straightforward as5 blocks that are 1 MB in size are written to the local storage 330,334, 338 resources and the block-storage 342, 344, 346 resources thatare utilized by the cloud computing instances 340 a, 340 b, 340 n. Insuch an example, the software daemon 328, 332, 336 or some other moduleof computer program instructions that is executing on the particularcloud computing instance 340 a, 340 b, 340 n may be configured to: 1)create a first object that includes the first 1 MB of data and write thefirst object to the cloud-based object storage 348, 2) create a secondobject that includes the second 1 MB of data and write the second objectto the cloud-based object storage 348, 3) create a third object thatincludes the third 1 MB of data and write the third object to thecloud-based object storage 348, and so on. As such, in some embodiments,each object that is written to the cloud-based object storage 348 may beidentical (or nearly identical) in size. Readers will appreciate that insuch an example, metadata that is associated with the data itself may beincluded in each object (e.g., the first 1 MB of the object is data andthe remaining portion is metadata associated with the data).

Readers will appreciate that the cloud-based object storage 348 may beincorporated into the cloud-based storage system 318 to increase thedurability of the cloud-based storage system 318. Continuing with theexample described above where the cloud computing instances 340 a, 340b, 340 n are EC2 instances, readers will understand that EC2 instancesare only guaranteed to have a monthly uptime of 99.9% and data stored inthe local instance store only persists during the lifetime of the EC2instance. As such, relying on the cloud computing instances 340 a, 340b, 340 n with local storage 330, 334, 338 as the only source ofpersistent data storage in the cloud-based storage system 318 may resultin a relatively unreliable storage system. Likewise, EBS volumes aredesigned for 99.999% availability. As such, even relying on EBS as thepersistent data store in the cloud-based storage system 318 may resultin a storage system that is not sufficiently durable. Amazon S3,however, is designed to provide 99.999999999% durability, meaning that acloud-based storage system 318 that can incorporate S3 into its pool ofstorage is substantially more durable than various other options.

Readers will appreciate that while a cloud-based storage system 318 thatcan incorporate S3 into its pool of storage is substantially moredurable than various other options, utilizing S3 as the primary pool ofstorage may result in storage system that has relatively slow responsetimes and relatively long I/O latencies. As such, the cloud-basedstorage system 318 depicted in FIG. 3C not only stores data in S3 butthe cloud-based storage system 318 also stores data in local storage330, 334, 338 resources and block-storage 342, 344, 346 resources thatare utilized by the cloud computing instances 340 a, 340 b, 340 n, suchthat read operations can be serviced from local storage 330, 334, 338resources and the block-storage 342, 344, 346 resources that areutilized by the cloud computing instances 340 a, 340 b, 340 n, therebyreducing read latency when users of the cloud-based storage system 318attempt to read data from the cloud-based storage system 318.

In some embodiments, all data that is stored by the cloud-based storagesystem 318 may be stored in both: 1) the cloud-based object storage 348,and 2) at least one of the local storage 330, 334, 338 resources orblock-storage 342, 344, 346 resources that are utilized by the cloudcomputing instances 340 a, 340 b, 340 n. In such embodiments, the localstorage 330, 334, 338 resources and block-storage 342, 344, 346resources that are utilized by the cloud computing instances 340 a, 340b, 340 n may effectively operate as cache that generally includes alldata that is also stored in S3, such that all reads of data may beserviced by the cloud computing instances 340 a, 340 b, 340 n withoutrequiring the cloud computing instances 340 a, 340 b, 340 n to accessthe cloud-based object storage 348. Readers will appreciate that inother embodiments, however, all data that is stored by the cloud-basedstorage system 318 may be stored in the cloud-based object storage 348,but less than all data that is stored by the cloud-based storage system318 may be stored in at least one of the local storage 330, 334, 338resources or block-storage 342, 344, 346 resources that are utilized bythe cloud computing instances 340 a, 340 b, 340 n. In such an example,various policies may be utilized to determine which subset of the datathat is stored by the cloud-based storage system 318 should reside inboth: 1) the cloud-based object storage 348, and 2) at least one of thelocal storage 330, 334, 338 resources or block-storage 342, 344, 346resources that are utilized by the cloud computing instances 340 a, 340b, 340 n.

As described above, when the cloud computing instances 340 a, 340 b, 340n with local storage 330, 334, 338 are embodied as EC2 instances, thecloud computing instances 340 a, 340 b, 340 n with local storage 330,334, 338 are only guaranteed to have a monthly uptime of 99.9% and datastored in the local instance store only persists during the lifetime ofeach cloud computing instance 340 a, 340 b, 340 n with local storage330, 334, 338. As such, one or more modules of computer programinstructions that are executing within the cloud-based storage system318 (e.g., a monitoring module that is executing on its own EC2instance) may be designed to handle the failure of one or more of thecloud computing instances 340 a, 340 b, 340 n with local storage 330,334, 338. In such an example, the monitoring module may handle thefailure of one or more of the cloud computing instances 340 a, 340 b,340 n with local storage 330, 334, 338 by creating one or more new cloudcomputing instances with local storage, retrieving data that was storedon the failed cloud computing instances 340 a, 340 b, 340 n from thecloud-based object storage 348, and storing the data retrieved from thecloud-based object storage 348 in local storage on the newly createdcloud computing instances. Readers will appreciate that many variants ofthis process may be implemented.

Consider an example in which all cloud computing instances 340 a, 340 b,340 n with local storage 330, 334, 338 failed. In such an example, themonitoring module may create new cloud computing instances with localstorage, where high-bandwidth instances types are selected that allowfor the maximum data transfer rates between the newly createdhigh-bandwidth cloud computing instances with local storage and thecloud-based object storage 348. Readers will appreciate that instancestypes are selected that allow for the maximum data transfer ratesbetween the new cloud computing instances and the cloud-based objectstorage 348 such that the new high-bandwidth cloud computing instancescan be rehydrated with data from the cloud-based object storage 348 asquickly as possible. Once the new high-bandwidth cloud computinginstances are rehydrated with data from the cloud-based object storage348, less expensive lower-bandwidth cloud computing instances may becreated, data may be migrated to the less expensive lower-bandwidthcloud computing instances, and the high-bandwidth cloud computinginstances may be terminated.

Readers will appreciate that in some embodiments, the number of newcloud computing instances that are created may substantially exceed thenumber of cloud computing instances that are needed to locally store allof the data stored by the cloud-based storage system 318. The number ofnew cloud computing instances that are created may substantially exceedthe number of cloud computing instances that are needed to locally storeall of the data stored by the cloud-based storage system 318 in order tomore rapidly pull data from the cloud-based object storage 348 and intothe new cloud computing instances, as each new cloud computing instancecan (in parallel) retrieve some portion of the data stored by thecloud-based storage system 318. In such embodiments, once the datastored by the cloud-based storage system 318 has been pulled into thenewly created cloud computing instances, the data may be consolidatedwithin a subset of the newly created cloud computing instances and thosenewly created cloud computing instances that are excessive may beterminated.

Consider an example in which 1000 cloud computing instances are neededin order to locally store all valid data that users of the cloud-basedstorage system 318 have written to the cloud-based storage system 318.In such an example, assume that all 1,000 cloud computing instancesfail. In such an example, the monitoring module may cause 100,000 cloudcomputing instances to be created, where each cloud computing instanceis responsible for retrieving, from the cloud-based object storage 348,distinct 1/100,000th chunks of the valid data that users of thecloud-based storage system 318 have written to the cloud-based storagesystem 318 and locally storing the distinct chunk of the dataset that itretrieved. In such an example, because each of the 100,000 cloudcomputing instances can retrieve data from the cloud-based objectstorage 348 in parallel, the caching layer may be restored 100 timesfaster as compared to an embodiment where the monitoring module onlycreate 1000 replacement cloud computing instances. In such an example,over time the data that is stored locally in the 100,000 could beconsolidated into 1,000 cloud computing instances and the remaining99,000 cloud computing instances could be terminated.

Readers will appreciate that various performance aspects of thecloud-based storage system 318 may be monitored (e.g., by a monitoringmodule that is executing in an EC2 instance) such that the cloud-basedstorage system 318 can be scaled-up or scaled-out as needed. Consider anexample in which the monitoring module monitors the performance of thecould-based storage system 318 via communications with one or more ofthe cloud computing instances 320, 322 that each are used to support theexecution of a storage controller application 324, 326, via monitoringcommunications between cloud computing instances 320, 322, 340 a, 340 b,340 n, via monitoring communications between cloud computing instances320, 322, 340 a, 340 b, 340 n and the cloud-based object storage 348, orin some other way. In such an example, assume that the monitoring moduledetermines that the cloud computing instances 320, 322 that are used tosupport the execution of a storage controller application 324, 326 areundersized and not sufficiently servicing the I/O requests that areissued by users of the cloud-based storage system 318. In such anexample, the monitoring module may create a new, more powerful cloudcomputing instance (e.g., a cloud computing instance of a type thatincludes more processing power, more memory, etc. . . . ) that includesthe storage controller application such that the new, more powerfulcloud computing instance can begin operating as the primary controller.Likewise, if the monitoring module determines that the cloud computinginstances 320, 322 that are used to support the execution of a storagecontroller application 324, 326 are oversized and that cost savingscould be gained by switching to a smaller, less powerful cloud computinginstance, the monitoring module may create a new, less powerful (andless expensive) cloud computing instance that includes the storagecontroller application such that the new, less powerful cloud computinginstance can begin operating as the primary controller.

Consider, as an additional example of dynamically sizing the cloud-basedstorage system 318, an example in which the monitoring module determinesthat the utilization of the local storage that is collectively providedby the cloud computing instances 340 a, 340 b, 340 n has reached apredetermined utilization threshold (e.g., 95%). In such an example, themonitoring module may create additional cloud computing instances withlocal storage to expand the pool of local storage that is offered by thecloud computing instances. Alternatively, the monitoring module maycreate one or more new cloud computing instances that have largeramounts of local storage than the already existing cloud computinginstances 340 a, 340 b, 340 n, such that data stored in an alreadyexisting cloud computing instance 340 a, 340 b, 340 n can be migrated tothe one or more new cloud computing instances and the already existingcloud computing instance 340 a, 340 b, 340 n can be terminated, therebyexpanding the pool of local storage that is offered by the cloudcomputing instances. Likewise, if the pool of local storage that isoffered by the cloud computing instances is unnecessarily large, datacan be consolidated and some cloud computing instances can beterminated.

Readers will appreciate that the cloud-based storage system 318 may besized up and down automatically by a monitoring module applying apredetermined set of rules that may be relatively simple of relativelycomplicated. In fact, the monitoring module may not only take intoaccount the current state of the cloud-based storage system 318, but themonitoring module may also apply predictive policies that are based on,for example, observed behavior (e.g., every night from 10 PM until 6 AMusage of the storage system is relatively light), predeterminedfingerprints (e.g., every time a virtual desktop infrastructure adds 100virtual desktops, the number of IOPS directed to the storage systemincrease by X), and so on. In such an example, the dynamic scaling ofthe cloud-based storage system 318 may be based on current performancemetrics, predicted workloads, and many other factors, includingcombinations thereof.

Readers will further appreciate that because the cloud-based storagesystem 318 may be dynamically scaled, the cloud-based storage system 318may even operate in a way that is more dynamic. Consider the example ofgarbage collection. In a traditional storage system, the amount ofstorage is fixed. As such, at some point the storage system may beforced to perform garbage collection as the amount of available storagehas become so constrained that the storage system is on the verge ofrunning out of storage. In contrast, the cloud-based storage system 318described here can always ‘add’ additional storage (e.g., by adding morecloud computing instances with local storage). Because the cloud-basedstorage system 318 described here can always ‘add’ additional storage,the cloud-based storage system 318 can make more intelligent decisionsregarding when to perform garbage collection. For example, thecloud-based storage system 318 may implement a policy that garbagecollection only be performed when the number of IOPS being serviced bythe cloud-based storage system 318 falls below a certain level. In someembodiments, other system-level functions (e.g., deduplication,compression) may also be turned off and on in response to system load,given that the size of the cloud-based storage system 318 is notconstrained in the same way that traditional storage systems areconstrained.

Readers will appreciate that embodiments of the present disclosureresolve an issue with block-storage services offered by some cloudcomputing environments as some cloud computing environments only allowfor one cloud computing instance to connect to a block-storage volume ata single time. For example, in Amazon AWS, only a single EC2 instancemay be connected to an EBS volume. Through the use of EC2 instances withlocal storage, embodiments of the present disclosure can offermulti-connect capabilities where multiple EC2 instances can connect toanother EC2 instance with local storage (‘a drive instance’). In suchembodiments, the drive instances may include software executing withinthe drive instance that allows the drive instance to support I/Odirected to a particular volume from each connected EC2 instance. Assuch, some embodiments of the present disclosure may be embodied asmulti-connect block storage services that may not include all of thecomponents depicted in FIG. 3C.

In some embodiments, especially in embodiments where the cloud-basedobject storage 348 resources are embodied as Amazon S3, the cloud-basedstorage system 318 may include one or more modules (e.g., a module ofcomputer program instructions executing on an EC2 instance) that areconfigured to ensure that when the local storage of a particular cloudcomputing instance is rehydrated with data from S3, the appropriate datais actually in S3. This issue arises largely because S3 implements aneventual consistency model where, when overwriting an existing object,reads of the object will eventually (but not necessarily immediately)become consistent and will eventually (but not necessarily immediately)return the overwritten version of the object. To address this issue, insome embodiments of the present disclosure, objects in S3 are neveroverwritten. Instead, a traditional ‘overwrite’ would result in thecreation of the new object (that includes the updated version of thedata) and the eventual deletion of the old object (that includes theprevious version of the data).

In some embodiments of the present disclosure, as part of an attempt tonever (or almost never) overwrite an object, when data is written to S3the resultant object may be tagged with a sequence number. In someembodiments, these sequence numbers may be persisted elsewhere (e.g., ina database) such that at any point in time, the sequence numberassociated with the most up-to-date version of some piece of data can beknown. In such a way, a determination can be made as to whether S3 hasthe most recent version of some piece of data by merely reading thesequence number associated with an object—and without actually readingthe data from S3. The ability to make this determination may beparticularly important when a cloud computing instance with localstorage crashes, as it would be undesirable to rehydrate the localstorage of a replacement cloud computing instance with out-of-date data.In fact, because the cloud-based storage system 318 does not need toaccess the data to verify its validity, the data can stay encrypted andaccess charges can be avoided.

The storage systems described above may carry out intelligent databackup techniques through which data stored in the storage system may becopied and stored in a distinct location to avoid data loss in the eventof equipment failure or some other form of catastrophe. For example, thestorage systems described above may be configured to examine each backupto avoid restoring the storage system to an undesirable state. Consideran example in which malware infects the storage system. In such anexample, the storage system may include software resources 314 that canscan each backup to identify backups that were captured before themalware infected the storage system and those backups that were capturedafter the malware infected the storage system. In such an example, thestorage system may restore itself from a backup that does not includethe malware—or at least not restore the portions of a backup thatcontained the malware. In such an example, the storage system mayinclude software resources 314 that can scan each backup to identify thepresences of malware (or a virus, or some other undesirable), forexample, by identifying write operations that were serviced by thestorage system and originated from a network subnet that is suspected tohave delivered the malware, by identifying write operations that wereserviced by the storage system and originated from a user that issuspected to have delivered the malware, by identifying write operationsthat were serviced by the storage system and examining the content ofthe write operation against fingerprints of the malware, and in manyother ways.

Readers will further appreciate that the backups (often in the form ofone or more snapshots) may also be utilized to perform rapid recovery ofthe storage system. Consider an example in which the storage system isinfected with ransomware that locks users out of the storage system. Insuch an example, software resources 314 within the storage system may beconfigured to detect the presence of ransomware and may be furtherconfigured to restore the storage system to a point-in-time, using theretained backups, prior to the point-in-time at which the ransomwareinfected the storage system. In such an example, the presence ofransomware may be explicitly detected through the use of software toolsutilized by the system, through the use of a key (e.g., a USB drive)that is inserted into the storage system, or in a similar way. Likewise,the presence of ransomware may be inferred in response to systemactivity meeting a predetermined fingerprint such as, for example, noreads or writes coming into the system for a predetermined period oftime.

Readers will appreciate that the various components described above maybe grouped into one or more optimized computing packages as convergedinfrastructures. Such converged infrastructures may include pools ofcomputers, storage and networking resources that can be shared bymultiple applications and managed in a collective manner usingpolicy-driven processes. Such converged infrastructures may beimplemented with a converged infrastructure reference architecture, withstandalone appliances, with a software driven hyper-converged approach(e.g., hyper-converged infrastructures), or in other ways.

Readers will appreciate that the storage systems described above may beuseful for supporting various types of software applications. Forexample, the storage system 306 may be useful in supporting artificialintelligence (‘AI’) applications, database applications, DevOpsprojects, electronic design automation tools, event-driven softwareapplications, high performance computing applications, simulationapplications, high-speed data capture and analysis applications, machinelearning applications, media production applications, media servingapplications, picture archiving and communication systems (‘PACS’)applications, software development applications, virtual realityapplications, augmented reality applications, and many other types ofapplications by providing storage resources to such applications.

The storage systems described above may operate to support a widevariety of applications. In view of the fact that the storage systemsinclude compute resources, storage resources, and a wide variety ofother resources, the storage systems may be well suited to supportapplications that are resource intensive such as, for example, AIapplications. AI applications may be deployed in a variety of fields,including: predictive maintenance in manufacturing and related fields,healthcare applications such as patient data & risk analytics, retailand marketing deployments (e.g., search advertising, social mediaadvertising), supply chains solutions, fintech solutions such asbusiness analytics & reporting tools, operational deployments such asreal-time analytics tools, application performance management tools, ITinfrastructure management tools, and many others.

Such AI applications may enable devices to perceive their environmentand take actions that maximize their chance of success at some goal.Examples of such AI applications can include IBM Watson, MicrosoftOxford, Google DeepMind, Baidu Minwa, and others. The storage systemsdescribed above may also be well suited to support other types ofapplications that are resource intensive such as, for example, machinelearning applications. Machine learning applications may perform varioustypes of data analysis to automate analytical model building. Usingalgorithms that iteratively learn from data, machine learningapplications can enable computers to learn without being explicitlyprogrammed. One particular area of machine learning is referred to asreinforcement learning, which involves taking suitable actions tomaximize reward in a particular situation. Reinforcement learning may beemployed to find the best possible behavior or path that a particularsoftware application or machine should take in a specific situation.Reinforcement learning differs from other areas of machine learning(e.g., supervised learning, unsupervised learning) in that correctinput/output pairs need not be presented for reinforcement learning andsub-optimal actions need not be explicitly corrected.

In addition to the resources already described, the storage systemsdescribed above may also include graphics processing units (‘GPUs’),occasionally referred to as visual processing unit (‘VPUs’). Such GPUsmay be embodied as specialized electronic circuits that rapidlymanipulate and alter memory to accelerate the creation of images in aframe buffer intended for output to a display device. Such GPUs may beincluded within any of the computing devices that are part of thestorage systems described above, including as one of many individuallyscalable components of a storage system, where other examples ofindividually scalable components of such storage system can includestorage components, memory components, compute components (e.g., CPUs,FPGAs, ASICs), networking components, software components, and others.In addition to GPUs, the storage systems described above may alsoinclude neural network processors (‘NNPs’) for use in various aspects ofneural network processing. Such NNPs may be used in place of (or inaddition to) GPUs and may be also be independently scalable.

As described above, the storage systems described herein may beconfigured to support artificial intelligence applications, machinelearning applications, big data analytics applications, and many othertypes of applications. The rapid growth in these sort of applications isbeing driven by three technologies: deep learning (DL), GPU processors,and Big Data. Deep learning is a computing model that makes use ofmassively parallel neural networks inspired by the human brain. Insteadof experts handcrafting software, a deep learning model writes its ownsoftware by learning from lots of examples. Such GPUs may includethousands of cores that are well-suited to run algorithms that looselyrepresent the parallel nature of the human brain.

Advances in deep neural networks, including the development ofmulti-layer neural networks, have ignited a new wave of algorithms andtools for data scientists to tap into their data with artificialintelligence (AI). With improved algorithms, larger data sets, andvarious frameworks (including open-source software libraries for machinelearning across a range of tasks), data scientists are tackling new usecases like autonomous driving vehicles, natural language processing andunderstanding, computer vision, machine reasoning, strong AI, and manyothers. Applications of such techniques may include: machine andvehicular object detection, identification and avoidance; visualrecognition, classification and tagging; algorithmic financial tradingstrategy performance management; simultaneous localization and mapping;predictive maintenance of high-value machinery; prevention against cybersecurity threats, expertise automation; image recognition andclassification; question answering; robotics; text analytics(extraction, classification) and text generation and translation; andmany others. Applications of AI techniques has materialized in a widearray of products include, for example, Amazon Echo's speech recognitiontechnology that allows users to talk to their machines, GoogleTranslate™ which allows for machine-based language translation,Spotify's Discover Weekly that provides recommendations on new songs andartists that a user may like based on the user's usage and trafficanalysis, Quill's text generation offering that takes structured dataand turns it into narrative stories, Chatbots that provide real-time,contextually specific answers to questions in a dialog format, and manyothers.

Data is the heart of modern AI and deep learning algorithms. Beforetraining can begin, one problem that must be addressed revolves aroundcollecting the labeled data that is crucial for training an accurate AImodel. A full scale AI deployment may be required to continuouslycollect, clean, transform, label, and store large amounts of data.Adding additional high quality data points directly translates to moreaccurate models and better insights. Data samples may undergo a seriesof processing steps including, but not limited to: 1) ingesting the datafrom an external source into the training system and storing the data inraw form, 2) cleaning and transforming the data in a format convenientfor training, including linking data samples to the appropriate label,3) exploring parameters and models, quickly testing with a smallerdataset, and iterating to converge on the most promising models to pushinto the production cluster, 4) executing training phases to selectrandom batches of input data, including both new and older samples, andfeeding those into production GPU servers for computation to updatemodel parameters, and 5) evaluating including using a holdback portionof the data not used in training in order to evaluate model accuracy onthe holdout data. This lifecycle may apply for any type of parallelizedmachine learning, not just neural networks or deep learning. Forexample, standard machine learning frameworks may rely on CPUs insteadof GPUs but the data ingest and training workflows may be the same.Readers will appreciate that a single shared storage data hub creates acoordination point throughout the lifecycle without the need for extradata copies among the ingest, preprocessing, and training stages. Rarelyis the ingested data used for only one purpose, and shared storage givesthe flexibility to train multiple different models or apply traditionalanalytics to the data.

Readers will appreciate that each stage in the AI data pipeline may havevarying requirements from the data hub (e.g., the storage system orcollection of storage systems). Scale-out storage systems must deliveruncompromising performance for all manner of access types andpatterns—from small, metadata-heavy to large files, from random tosequential access patterns, and from low to high concurrency. Thestorage systems described above may serve as an ideal AI data hub as thesystems may service unstructured workloads. In the first stage, data isideally ingested and stored on to the same data hub that followingstages will use, in order to avoid excess data copying. The next twosteps can be done on a standard compute server that optionally includesa GPU, and then in the fourth and last stage, full training productionjobs are run on powerful GPU-accelerated servers. Often, there is aproduction pipeline alongside an experimental pipeline operating on thesame dataset. Further, the GPU-accelerated servers can be usedindependently for different models or joined together to train on onelarger model, even spanning multiple systems for distributed training.If the shared storage tier is slow, then data must be copied to localstorage for each phase, resulting in wasted time staging data ontodifferent servers. The ideal data hub for the AI training pipelinedelivers performance similar to data stored locally on the server nodewhile also having the simplicity and performance to enable all pipelinestages to operate concurrently.

In order for the storage systems described above to serve as a data hubor as part of an AI deployment, in some embodiments the storage systemsmay be configured to provide DMA between storage devices that areincluded in the storage systems and one or more GPUs that are used in anAI or big data analytics pipeline. The one or more GPUs may be coupledto the storage system, for example, via NVMe-over-Fabrics (‘NVMe-oF’)such that bottlenecks such as the host CPU can be bypassed and thestorage system (or one of the components contained therein) can directlyaccess GPU memory. In such an example, the storage systems may leverageAPI hooks to the GPUs to transfer data directly to the GPUs. Forexample, the GPUs may be embodied as Nvidia™ GPUs and the storagesystems may support GPUDirect Storage (‘GDS’) software, or have similarproprietary software, that enables the storage system to transfer datato the GPUs via RDMA or similar mechanism. Readers will appreciate thatin embodiments where the storage systems are embodied as cloud-basedstorage systems as described below, virtual drive or other componentswithin such a cloud-based storage system may also be configured

Although the preceding paragraphs discuss deep learning applications,readers will appreciate that the storage systems described herein mayalso be part of a distributed deep learning (‘DDL’) platform to supportthe execution of DDL algorithms. The storage systems described above mayalso be paired with other technologies such as TensorFlow, anopen-source software library for dataflow programming across a range oftasks that may be used for machine learning applications such as neuralnetworks, to facilitate the development of such machine learning models,applications, and so on.

The storage systems described above may also be used in a neuromorphiccomputing environment. Neuromorphic computing is a form of computingthat mimics brain cells. To support neuromorphic computing, anarchitecture of interconnected “neurons” replace traditional computingmodels with low-powered signals that go directly between neurons formore efficient computation. Neuromorphic computing may make use ofvery-large-scale integration (VLSI) systems containing electronic analogcircuits to mimic neuro-biological architectures present in the nervoussystem, as well as analog, digital, mixed-mode analog/digital VLSI, andsoftware systems that implement models of neural systems for perception,motor control, or multisensory integration.

Readers will appreciate that the storage systems described above may beconfigured to support the storage or use of (among other types of data)blockchains. In addition to supporting the storage and use of blockchaintechnologies, the storage systems described above may also support thestorage and use of derivative items such as, for example, open sourceblockchains and related tools that are part of the IBM™ Hyperledgerproject, permissioned blockchains in which a certain number of trustedparties are allowed to access the block chain, blockchain products thatenable developers to build their own distributed ledger projects, andothers. Blockchains and the storage systems described herein may beleveraged to support on-chain storage of data as well as off-chainstorage of data.

Off-chain storage of data can be implemented in a variety of ways andcan occur when the data itself is not stored within the blockchain. Forexample, in one embodiment, a hash function may be utilized and the dataitself may be fed into the hash function to generate a hash value. Insuch an example, the hashes of large pieces of data may be embeddedwithin transactions, instead of the data itself. Readers will appreciatethat, in other embodiments, alternatives to blockchains may be used tofacilitate the decentralized storage of information. For example, onealternative to a blockchain that may be used is a blockweave. Whileconventional blockchains store every transaction to achieve validation,a blockweave permits secure decentralization without the usage of theentire chain, thereby enabling low cost on-chain storage of data. Suchblockweaves may utilize a consensus mechanism that is based on proof ofaccess (PoA) and proof of work (PoW).

The storage systems described above may, either alone or in combinationwith other computing devices, be used to support in-memory computingapplications. In-memory computing involves the storage of information inRAM that is distributed across a cluster of computers. Readers willappreciate that the storage systems described above, especially thosethat are configurable with customizable amounts of processing resources,storage resources, and memory resources (e.g., those systems in whichblades that contain configurable amounts of each type of resource), maybe configured in a way so as to provide an infrastructure that cansupport in-memory computing. Likewise, the storage systems describedabove may include component parts (e.g., NVDIMMs, 3D crosspoint storagethat provide fast random access memory that is persistent) that canactually provide for an improved in-memory computing environment ascompared to in-memory computing environments that rely on RAMdistributed across dedicated servers.

In some embodiments, the storage systems described above may beconfigured to operate as a hybrid in-memory computing environment thatincludes a universal interface to all storage media (e.g., RAM, flashstorage, 3D crosspoint storage). In such embodiments, users may have noknowledge regarding the details of where their data is stored but theycan still use the same full, unified API to address data. In suchembodiments, the storage system may (in the background) move data to thefastest layer available—including intelligently placing the data independence upon various characteristics of the data or in dependenceupon some other heuristic. In such an example, the storage systems mayeven make use of existing products such as Apache Ignite and GridGain tomove data between the various storage layers, or the storage systems maymake use of custom software to move data between the various storagelayers. The storage systems described herein may implement variousoptimizations to improve the performance of in-memory computing such as,for example, having computations occur as close to the data as possible.

Readers will further appreciate that in some embodiments, the storagesystems described above may be paired with other resources to supportthe applications described above. For example, one infrastructure couldinclude primary compute in the form of servers and workstations whichspecialize in using General-purpose computing on graphics processingunits (‘GPGPU’) to accelerate deep learning applications that areinterconnected into a computation engine to train parameters for deepneural networks. Each system may have Ethernet external connectivity,InfiniBand external connectivity, some other form of externalconnectivity, or some combination thereof. In such an example, the GPUscan be grouped for a single large training or used independently totrain multiple models. The infrastructure could also include a storagesystem such as those described above to provide, for example, ascale-out all-flash file or object store through which data can beaccessed via high-performance protocols such as NFS, S3, and so on. Theinfrastructure can also include, for example, redundant top-of-rackEthernet switches connected to storage and compute via ports in MLAGport channels for redundancy. The infrastructure could also includeadditional compute in the form of whitebox servers, optionally withGPUs, for data ingestion, pre-processing, and model debugging. Readerswill appreciate that additional infrastructures are also be possible.

Readers will appreciate that the storage systems described above, eitheralone or in coordination with other computing machinery may beconfigured to support other AI related tools. For example, the storagesystems may make use of tools like ONXX or other open neural networkexchange formats that make it easier to transfer models written indifferent AI frameworks. Likewise, the storage systems may be configuredto support tools like Amazon's Gluon that allow developers to prototype,build, and train deep learning models. In fact, the storage systemsdescribed above may be part of a larger platform, such as IBM™ CloudPrivate for Data, that includes integrated data science, dataengineering and application building services.

Readers will further appreciate that the storage systems described abovemay also be deployed as an edge solution. Such an edge solution may bein place to optimize cloud computing systems by performing dataprocessing at the edge of the network, near the source of the data. Edgecomputing can push applications, data and computing power (i.e.,services) away from centralized points to the logical extremes of anetwork. Through the use of edge solutions such as the storage systemsdescribed above, computational tasks may be performed using the computeresources provided by such storage systems, data may be storage usingthe storage resources of the storage system, and cloud-based servicesmay be accessed through the use of various resources of the storagesystem (including networking resources). By performing computationaltasks on the edge solution, storing data on the edge solution, andgenerally making use of the edge solution, the consumption of expensivecloud-based resources may be avoided and, in fact, performanceimprovements may be experienced relative to a heavier reliance oncloud-based resources.

While many tasks may benefit from the utilization of an edge solution,some particular uses may be especially suited for deployment in such anenvironment. For example, devices like drones, autonomous cars, robots,and others may require extremely rapid processing—so fast, in fact, thatsending data up to a cloud environment and back to receive dataprocessing support may simply be too slow. As an additional example,some IoT devices such as connected video cameras may not be well-suitedfor the utilization of cloud-based resources as it may be impractical(not only from a privacy perspective, security perspective, or afinancial perspective) to send the data to the cloud simply because ofthe pure volume of data that is involved. As such, many tasks thatreally on data processing, storage, or communications may be bettersuited by platforms that include edge solutions such as the storagesystems described above.

The storage systems described above may alone, or in combination withother computing resources, serves as a network edge platform thatcombines compute resources, storage resources, networking resources,cloud technologies and network virtualization technologies, and so on.As part of the network, the edge may take on characteristics similar toother network facilities, from the customer premise and backhaulaggregation facilities to Points of Presence (PoPs) and regional datacenters. Readers will appreciate that network workloads, such as VirtualNetwork Functions (VNFs) and others, will reside on the network edgeplatform. Enabled by a combination of containers and virtual machines,the network edge platform may rely on controllers and schedulers thatare no longer geographically co-located with the data processingresources. The functions, as microservices, may split into controlplanes, user and data planes, or even state machines, allowing forindependent optimization and scaling techniques to be applied. Such userand data planes may be enabled through increased accelerators, boththose residing in server platforms, such as FPGAs and Smart NICs, andthrough SDN-enabled merchant silicon and programmable ASICs.

The storage systems described above may also be optimized for use in bigdata analytics. Big data analytics may be generally described as theprocess of examining large and varied data sets to uncover hiddenpatterns, unknown correlations, market trends, customer preferences andother useful information that can help organizations make more-informedbusiness decisions. As part of that process, semi-structured andunstructured data such as, for example, internet clickstream data, webserver logs, social media content, text from customer emails and surveyresponses, mobile-phone call-detail records, IoT sensor data, and otherdata may be converted to a structured form.

The storage systems described above may also support (includingimplementing as a system interface) applications that perform tasks inresponse to human speech. For example, the storage systems may supportthe execution intelligent personal assistant applications such as, forexample, Amazon's Alexa, Apple Siri, Google Voice, Samsung Bixby,Microsoft Cortana, and others. While the examples described in theprevious sentence make use of voice as input, the storage systemsdescribed above may also support chatbots, talkbots, chatterbots, orartificial conversational entities or other applications that areconfigured to conduct a conversation via auditory or textual methods.Likewise, the storage system may actually execute such an application toenable a user such as a system administrator to interact with thestorage system via speech. Such applications are generally capable ofvoice interaction, music playback, making to-do lists, setting alarms,streaming podcasts, playing audiobooks, and providing weather, traffic,and other real time information, such as news, although in embodimentsin accordance with the present disclosure, such applications may beutilized as interfaces to various system management operations.

The storage systems described above may also implement AI platforms fordelivering on the vision of self-driving storage. Such AI platforms maybe configured to deliver global predictive intelligence by collectingand analyzing large amounts of storage system telemetry data points toenable effortless management, analytics and support. In fact, suchstorage systems may be capable of predicting both capacity andperformance, as well as generating intelligent advice on workloaddeployment, interaction and optimization. Such AI platforms may beconfigured to scan all incoming storage system telemetry data against alibrary of issue fingerprints to predict and resolve incidents inreal-time, before they impact customer environments, and captureshundreds of variables related to performance that are used to forecastperformance load.

The storage systems described above may support the serialized orsimultaneous execution of artificial intelligence applications, machinelearning applications, data analytics applications, datatransformations, and other tasks that collectively may form an AIladder. Such an AI ladder may effectively be formed by combining suchelements to form a complete data science pipeline, where existdependencies between elements of the AI ladder. For example, AI mayrequire that some form of machine learning has taken place, machinelearning may require that some form of analytics has taken place,analytics may require that some form of data and informationarchitecting has taken place, and so on. As such, each element may beviewed as a rung in an AI ladder that collectively can form a completeand sophisticated AI solution.

The storage systems described above may also, either alone or incombination with other computing environments, be used to deliver an AIeverywhere experience where AI permeates wide and expansive aspects ofbusiness and life. For example, AI may play an important role in thedelivery of deep learning solutions, deep reinforcement learningsolutions, artificial general intelligence solutions, autonomousvehicles, cognitive computing solutions, commercial UAVs or drones,conversational user interfaces, enterprise taxonomies, ontologymanagement solutions, machine learning solutions, smart dust, smartrobots, smart workplaces, and many others.

The storage systems described above may also, either alone or incombination with other computing environments, be used to deliver a widerange of transparently immersive experiences (including those that usedigital twins of various “things” such as people, places, processes,systems, and so on) where technology can introduce transparency betweenpeople, businesses, and things. Such transparently immersive experiencesmay be delivered as augmented reality technologies, connected homes,virtual reality technologies, brain-computer interfaces, humanaugmentation technologies, nanotube electronics, volumetric displays, 4Dprinting technologies, or others.

The storage systems described above may also, either alone or incombination with other computing environments, be used to support a widevariety of digital platforms. Such digital platforms can include, forexample, 5G wireless systems and platforms, digital twin platforms, edgecomputing platforms, IoT platforms, quantum computing platforms,serverless PaaS, software-defined security, neuromorphic computingplatforms, and so on.

The storage systems described above may also be part of a multi-cloudenvironment in which multiple cloud computing and storage services aredeployed in a single heterogeneous architecture. In order to facilitatethe operation of such a multi-cloud environment, DevOps tools may bedeployed to enable orchestration across clouds. Likewise, continuousdevelopment and continuous integration tools may be deployed tostandardize processes around continuous integration and delivery, newfeature rollout and provisioning cloud workloads. By standardizing theseprocesses, a multi-cloud strategy may be implemented that enables theutilization of the best provider for each workload.

The storage systems described above may be used as a part of a platformto enable the use of crypto-anchors that may be used to authenticate aproduct's origins and contents to ensure that it matches a blockchainrecord associated with the product. Similarly, as part of a suite oftools to secure data stored on the storage system, the storage systemsdescribed above may implement various encryption technologies andschemes, including lattice cryptography. Lattice cryptography caninvolve constructions of cryptographic primitives that involve lattices,either in the construction itself or in the security proof. Unlikepublic-key schemes such as the RSA, Diffie-Hellman or Elliptic-Curvecryptosystems, which are easily attacked by a quantum computer, somelattice-based constructions appear to be resistant to attack by bothclassical and quantum computers.

A quantum computer is a device that performs quantum computing. Quantumcomputing is computing using quantum-mechanical phenomena, such assuperposition and entanglement. Quantum computers differ fromtraditional computers that are based on transistors, as such traditionalcomputers require that data be encoded into binary digits (bits), eachof which is always in one of two definite states (0 or 1). In contrastto traditional computers, quantum computers use quantum bits, which canbe in superpositions of states. A quantum computer maintains a sequenceof qubits, where a single qubit can represent a one, a zero, or anyquantum superposition of those two qubit states. A pair of qubits can bein any quantum superposition of 4 states, and three qubits in anysuperposition of 8 states. A quantum computer with n qubits cangenerally be in an arbitrary superposition of up to 2{circumflex over( )}n different states simultaneously, whereas a traditional computercan only be in one of these states at any one time. A quantum Turingmachine is a theoretical model of such a computer.

The storage systems described above may also be paired withFPGA-accelerated servers as part of a larger AI or ML infrastructure.Such FPGA-accelerated servers may reside near (e.g., in the same datacenter) the storage systems described above or even incorporated into anappliance that includes one or more storage systems, one or moreFPGA-accelerated servers, networking infrastructure that supportscommunications between the one or more storage systems and the one ormore FPGA-accelerated servers, as well as other hardware and softwarecomponents. Alternatively, FPGA-accelerated servers may reside within acloud computing environment that may be used to perform compute-relatedtasks for AI and ML jobs. Any of the embodiments described above may beused to collectively serve as a FPGA-based AI or ML platform. Readerswill appreciate that, in some embodiments of the FPGA-based AI or MLplatform, the FPGAs that are contained within the FPGA-acceleratedservers may be reconfigured for different types of ML models (e.g.,LSTMs, CNNs, GRUs). The ability to reconfigure the FPGAs that arecontained within the FPGA-accelerated servers may enable theacceleration of a ML or AI application based on the most optimalnumerical precision and memory model being used. Readers will appreciatethat by treating the collection of FPGA-accelerated servers as a pool ofFPGAs, any CPU in the data center may utilize the pool of FPGAs as ashared hardware microservice, rather than limiting a server to dedicatedaccelerators plugged into it.

The FPGA-accelerated servers and the GPU-accelerated servers describedabove may implement a model of computing where, rather than keeping asmall amount of data in a CPU and running a long stream of instructionsover it as occurred in more traditional computing models, the machinelearning model and parameters are pinned into the high-bandwidth on-chipmemory with lots of data streaming though the high-bandwidth on-chipmemory. FPGAs may even be more efficient than GPUs for this computingmodel, as the FPGAs can be programmed with only the instructions neededto run this kind of computing model.

The storage systems described above may be configured to provideparallel storage, for example, through the use of a parallel file systemsuch as BeeGFS. Such parallel files systems may include a distributedmetadata architecture. For example, the parallel file system may includea plurality of metadata servers across which metadata is distributed, aswell as components that include services for clients and storageservers.

The systems described above can support the execution of a wide array ofsoftware applications. Such software applications can be deployed in avariety of ways, including container-based deployment models.Containerized applications may be managed using a variety of tools. Forexample, containerized applications may be managed using Docker Swarm,Kubernetes, and others. Containerized applications may be used tofacilitate a serverless, cloud native computing deployment andmanagement model for software applications. In support of a serverless,cloud native computing deployment and management model for softwareapplications, containers may be used as part of an event handlingmechanisms (e.g., AWS Lambdas) such that various events cause acontainerized application to be spun up to operate as an event handler.

The systems described above may be deployed in a variety of ways,including being deployed in ways that support fifth generation (‘5G’)networks. 5G networks may support substantially faster datacommunications than previous generations of mobile communicationsnetworks and, as a consequence may lead to the disaggregation of dataand computing resources as modern massive data centers may become lessprominent and may be replaced, for example, by more-local, micro datacenters that are close to the mobile-network towers. The systemsdescribed above may be included in such local, micro data centers andmay be part of or paired to multi-access edge computing (‘MEC’) systems.Such MEC systems may enable cloud computing capabilities and an ITservice environment at the edge of the cellular network. By runningapplications and performing related processing tasks closer to thecellular customer, network congestion may be reduced and applicationsmay perform better.

The storage systems described above may also be configured to implementNVMe Zoned Namespaces. Through the use of NVMe Zoned Namespaces, thelogical address space of a namespace is divided into zones. Each zoneprovides a logical block address range that must be written sequentiallyand explicitly reset before rewriting, thereby enabling the creation ofnamespaces that expose the natural boundaries of the device and offloadmanagement of internal mapping tables to the host. In order to implementNVMe Zoned Name Spaces (‘ZNS’), ZNS SSDs or some other form of zonedblock devices may be utilized that expose a namespace logical addressspace using zones. With the zones aligned to the internal physicalproperties of the device, several inefficiencies in the placement ofdata can be eliminated. In such embodiments, each zone may be mapped,for example, to a separate application such that functions like wearlevelling and garbage collection could be performed on a per-zone orper-application basis rather than across the entire device. In order tosupport ZNS, the storage controllers described herein may be configuredwith to interact with zoned block devices through the usage of, forexample, the Linux™ kernel zoned block device interface or other tools.

The storage systems described above may also be configured to implementzoned storage in other ways such as, for example, through the usage ofshingled magnetic recording (SMR) storage devices. In examples wherezoned storage is used, device-managed embodiments may be deployed wherethe storage devices hide this complexity by managing it in the firmware,presenting an interface like any other storage device. Alternatively,zoned storage may be implemented via a host-managed embodiment thatdepends on the operating system to know how to handle the drive, andonly write sequentially to certain regions of the drive. Zoned storagemay similarly be implemented using a host-aware embodiment in which acombination of a drive managed and host managed implementation isdeployed.

For further explanation, FIG. 3D illustrates an exemplary computingdevice 350 that may be specifically configured to perform one or more ofthe processes described herein. As shown in FIG. 3D, computing device350 may include a communication interface 352, a processor 354, astorage device 356, and an input/output (“I/O”) module 358communicatively connected one to another via a communicationinfrastructure 360. While an exemplary computing device 350 is shown inFIG. 3D, the components illustrated in FIG. 3D are not intended to belimiting. Additional or alternative components may be used in otherembodiments. Components of computing device 350 shown in FIG. 3D willnow be described in additional detail.

Communication interface 352 may be configured to communicate with one ormore computing devices. Examples of communication interface 352 include,without limitation, a wired network interface (such as a networkinterface card), a wireless network interface (such as a wirelessnetwork interface card), a modem, an audio/video connection, and anyother suitable interface.

Processor 354 generally represents any type or form of processing unitcapable of processing data and/or interpreting, executing, and/ordirecting execution of one or more of the instructions, processes,and/or operations described herein. Processor 354 may perform operationsby executing computer-executable instructions 362 (e.g., an application,software, code, and/or other executable data instance) stored in storagedevice 356.

Storage device 356 may include one or more data storage media, devices,or configurations and may employ any type, form, and combination of datastorage media and/or device. For example, storage device 356 mayinclude, but is not limited to, any combination of the non-volatilemedia and/or volatile media described herein. Electronic data, includingdata described herein, may be temporarily and/or permanently stored instorage device 356. For example, data representative ofcomputer-executable instructions 362 configured to direct processor 354to perform any of the operations described herein may be stored withinstorage device 356. In some examples, data may be arranged in one ormore databases residing within storage device 356.

I/O module 358 may include one or more I/O modules configured to receiveuser input and provide user output. I/O module 358 may include anyhardware, firmware, software, or combination thereof supportive of inputand output capabilities. For example, I/O module 358 may includehardware and/or software for capturing user input, including, but notlimited to, a keyboard or keypad, a touchscreen component (e.g.,touchscreen display), a receiver (e.g., an RF or infrared receiver),motion sensors, and/or one or more input buttons.

I/O module 358 may include one or more devices for presenting output toa user, including, but not limited to, a graphics engine, a display(e.g., a display screen), one or more output drivers (e.g., displaydrivers), one or more audio speakers, and one or more audio drivers. Incertain embodiments, I/O module 358 is configured to provide graphicaldata to a display for presentation to a user. The graphical data may berepresentative of one or more graphical user interfaces and/or any othergraphical content as may serve a particular implementation. In someexamples, any of the systems, computing devices, and/or other componentsdescribed herein may be implemented by computing device 350.

The storage systems described above may, either alone or in combination,by configured to serve as a continuous data protection store. Acontinuous data protection store is a feature of a storage system thatrecords updates to a dataset in such a way that consistent images ofprior contents of the dataset can be accessed with a low timegranularity (often on the order of seconds, or even less), andstretching back for a reasonable period of time (often hours or days).These allow access to very recent consistent points in time for thedataset, and also allow access to access to points in time for a datasetthat might have just preceded some event that, for example, caused partsof the dataset to be corrupted or otherwise lost, while retaining closeto the maximum number of updates that preceded that event. Conceptually,they are like a sequence of snapshots of a dataset taken very frequentlyand kept for a long period of time, though continuous data protectionstores are often implemented quite differently from snapshots. A storagesystem implementing a data continuous data protection store may furtherprovide a means of accessing these points in time, accessing one or moreof these points in time as snapshots or as cloned copies, or revertingthe dataset back to one of those recorded points in time.

Over time, to reduce overhead, some points in the time held in acontinuous data protection store can be merged with other nearby pointsin time, essentially deleting some of these points in time from thestore. This can reduce the capacity needed to store updates. It may alsobe possible to convert a limited number of these points in time intolonger duration snapshots. For example, such a store might keep a lowgranularity sequence of points in time stretching back a few hours fromthe present, with some points in time merged or deleted to reduceoverhead for up to an additional day. Stretching back in the pastfurther than that, some of these points in time could be converted tosnapshots representing consistent point-in-time images from only everyfew hours.

Although some embodiments are described largely in the context of astorage system, readers of skill in the art will recognize thatembodiments of the present disclosure may also take the form of acomputer program product disposed upon computer readable storage mediafor use with any suitable processing system. Such computer readablestorage media may be any storage medium for machine-readableinformation, including magnetic media, optical media, solid-state media,or other suitable media. Examples of such media include magnetic disksin hard drives or diskettes, compact disks for optical drives, magnetictape, and others as will occur to those of skill in the art. Personsskilled in the art will immediately recognize that any computer systemhaving suitable programming means will be capable of executing the stepsdescribed herein as embodied in a computer program product. Personsskilled in the art will recognize also that, although some of theembodiments described in this specification are oriented to softwareinstalled and executing on computer hardware, nevertheless, alternativeembodiments implemented as firmware or as hardware are well within thescope of the present disclosure.

In some examples, a non-transitory computer-readable medium storingcomputer-readable instructions may be provided in accordance with theprinciples described herein. The instructions, when executed by aprocessor of a computing device, may direct the processor and/orcomputing device to perform one or more operations, including one ormore of the operations described herein. Such instructions may be storedand/or transmitted using any of a variety of known computer-readablemedia.

A non-transitory computer-readable medium as referred to herein mayinclude any non-transitory storage medium that participates in providingdata (e.g., instructions) that may be read and/or executed by acomputing device (e.g., by a processor of a computing device). Forexample, a non-transitory computer-readable medium may include, but isnot limited to, any combination of non-volatile storage media and/orvolatile storage media. Exemplary non-volatile storage media include,but are not limited to, read-only memory, flash memory, a solid-statedrive, a magnetic storage device (e.g. a hard disk, a floppy disk,magnetic tape, etc.), ferroelectric random-access memory (“RAM”), and anoptical disc (e.g., a compact disc, a digital video disc, a Blu-raydisc, etc.). Exemplary volatile storage media include, but are notlimited to, RAM (e.g., dynamic RAM).

Advantages and features of the present disclosure can be furtherdescribed by the following statements:

1. A method comprising: detecting, by a storage management system, anevent within a storage system; determining, by the storage managementsystem based on the event, an operation related to a compliance rulesetassociated with a compliance policy; and providing, by the storagemanagement system, a notification of the operation to an orchestrationsystem configured to manage an execution of the operation by a computingsystem associated with the storage system.

2. The method of any of the preceding statements, wherein: theorchestration system is distinct from the storage management system; andthe orchestration system is unaware of one or more events within thestorage system.

3. The method of any of the preceding statements, wherein: the storagesystem is a file system configured to store one or more data files.

4. The method of any of the preceding statements, wherein: thedetermining of the operation includes determining, based on the event,at least one of a serverless function, a processing task, or a statefulapplication associated with the compliance ruleset; and the notificationof the operation specifies the at least one of the serverless function,the processing task, or the stateful application to be executed by thecomputing system.

5. The method of any of the preceding statements, wherein: the eventwithin the storage system includes one or more interactions with one ormore data items of a dataset stored in the storage system; and theoperation executed by the computing system includes determining acompliance level of the dataset with the compliance ruleset.

6. The method of any of the preceding statements, wherein: the eventwithin the storage system includes one or more attribute changesassociated with one or more data items of a dataset stored in thestorage system; and the operation executed by the computing systemincludes determining a compliance level of the dataset with thecompliance ruleset.

7. The method of any of the preceding statements, wherein: the eventwithin the storage system includes a mounting of a storage volume in thestorage system to a node in the computing system or an unmounting of thestorage volume in the storage system from the node in the computingsystem; and the operation executed by the computing system includesdetermining one or more compliance levels of one or more datasets storedin the storage volume with the compliance ruleset.

8. The method of any of the preceding statements, wherein: the eventwithin the storage system includes a detection that a previouscompliance ruleset associated with the compliance policy is modifiedinto the compliance ruleset; and the operation executed by the computingsystem includes determining one or more compliance levels of one or moredatasets stored in the storage system with the compliance ruleset.

9. The method of any of the preceding statements, wherein: the eventwithin the storage system includes an ingestion of a dataset into thestorage system; and the operation executed by the computing systemincludes generating an attribute model for the dataset, the attributemodel being used to determine a compliance level of the dataset with thecompliance ruleset.

10. The method of any of the preceding statements, further comprising:detecting, by the storage management system, an additional event withinthe storage system, the additional event being associated with theoperation; determining, by the storage management system, an additionaloperation based on the additional event; and providing, by the storagemanagement system, an additional notification of the additionaloperation to the orchestration system configured to manage an executionof the additional operation by the computing system.

11. The method of any of the preceding statements, wherein: theoperation executed by the computing system includes generating anattribute model for a dataset; the additional event within the storagesystem includes a storing of the attribute model within the storagesystem; and the additional operation executed by the computing systemincludes determining a compliance level of the dataset with thecompliance ruleset based on the attribute model.

12. The method of any of the preceding statements, wherein: theoperation executed by the computing system includes determining acompliance level of a dataset in the storage system with the complianceruleset; the additional event within the storage system includes astoring of the compliance level of the dataset within the storagesystem; and the additional operation executed by the computing systemincludes analyzing the compliance level of the dataset.

13. The method of any of the preceding statements, wherein the analyzingof the compliance level of the dataset includes: determining that thecompliance level of the dataset satisfies a compliance level threshold;and performing, based on the determining that the compliance level ofthe dataset satisfies the compliance level threshold, an action withrespect to the dataset.

14. The method of any of the preceding statements, wherein the actionwith respect to the dataset includes one or more of: presenting, to auser, a noncompliance notification associated with the dataset;presenting, to the user, a recommendation including one or more remedialactions to conform the dataset with the compliance ruleset; orautomatically performing the one or more remedial actions for thedataset.

15. A system comprising: a memory storing instructions; and a processorcommunicatively coupled to the memory and configured to execute theinstructions to: detect an event within a storage system; determine,based on the event, an operation related to a compliance rulesetassociated with a compliance policy; and provide a notification of theoperation to an orchestration system configured to manage an executionof the operation by a computing system associated with the storagesystem.

16. The system of the statement 15, wherein: the orchestration system isdistinct from the system; and the orchestration system is unaware of oneor more events within the storage system.

17. The system of any of the statements 15-16, wherein: the storagesystem is a file system configured to store one or more data files.

18. The system of any of the statements 15-17, wherein: the determiningof the operation includes determining, based on the event, at least oneof a serverless function, a processing task, or a stateful applicationassociated with the compliance ruleset; and the notification of theoperation specifies the at least one of the serverless function, theprocessing task, or the stateful application to be executed by thecomputing system.

19. The system of any of the statements 15-18, wherein: the event withinthe storage system includes a detection that a previous complianceruleset associated with the compliance policy is modified into thecompliance ruleset; and the operation executed by the computing systemincludes determining one or more compliance levels of one or moredatasets stored in the storage system with the compliance ruleset.

20. A non-transitory computer-readable medium storing instructions that,when executed, direct a processor of a computing device to: detect anevent within a storage system; determine, based on the event, anoperation related to a compliance ruleset associated with a compliancepolicy; and provide a notification of the operation to an orchestrationsystem configured to manage an execution of the operation by a computingsystem associated with the storage system.

One or more embodiments may be described herein with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claims. Further, the boundariesof these functional building blocks have been arbitrarily defined forconvenience of description. Alternate boundaries could be defined aslong as the certain significant functions are appropriately performed.Similarly, flow diagram blocks may also have been arbitrarily definedherein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence couldhave been defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claims. One of average skill in the art will alsorecognize that the functional building blocks, and other illustrativeblocks, modules and components herein, can be implemented as illustratedor by discrete components, application specific integrated circuits,processors executing appropriate software and the like or anycombination thereof.

While particular combinations of various functions and features of theone or more embodiments are expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent disclosure is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

In some embodiments, any of the storage systems described herein may beused to store data at rest. Data at rest may refer to data that, for atleast a period of time, is not accessed and not transmitted within asystem or network. Instead, the data at rest may be inactive and may bestored within a storage system (e.g., a cloud-based storage system, alocal storage system, a cloud-based backup system, etc.) as archiveddata, backup data, and/or stationary data for other purposes.

In some embodiments, the data at rest may be required to conform withone or more compliance policies enforced by a government agency and/oran organization (e.g., a company, a professional institution, etc.). Forexample, a compliance policy may require a particular type of data(e.g., personally identifiable information such as address, phonenumber, etc.) to be generalized or anonymized to protect user privacy.In some embodiments, when the storage system receives and stores data atrest, the data may be modified or adjusted to comply with the compliancepolicy. However, the compliance policy may change over time. Due tothese changes, the data stored at rest in the storage system may nolonger comply with the compliance policy that is currently in effect.

As described herein, a monitoring system associated with a storagesystem may obtain an attribute model for a dataset stored at rest withinthe storage system. In some examples, the attribute model of the datasetmay be generated when the dataset is ingested into the storage systemand may indicate one or more attributes of the dataset. Alternatively,the attribute model of the dataset may be generated by scanning thedataset at a time after the dataset is ingested. The time at which thedataset is scanned to generate the attribute model may be selected basedon data ingest performance, a scanning speed, a processing speed togenerate the attribute model, a total cost (e.g., a storage cost, anetwork cost, etc.) for performing the scan, and/or other factors. Insome examples, the monitoring system may determine, based on theattribute model, a compliance level of the dataset with a complianceruleset associated with a compliance policy. In some examples, thecompliance ruleset may be associated with a version of the compliancepolicy that is up to date and currently enforced by a particulargovernment agency and/or organization.

In some examples, based on the compliance level of the dataset with thecompliance ruleset, the monitoring system may perform an operation withrespect to the dataset. For example, if the compliance level of thedataset with the compliance ruleset is lower than a predefinedcompliance level threshold, the monitoring system may determine that thedataset no longer complies with the compliance ruleset specified by thecompliance policy. Therefore, the monitoring system may present anotification associated with the dataset to inform an authorized userabout such noncompliance, provide a recommendation including one or moreremedial actions to conform the dataset to the compliance ruleset,automatically perform the one or more remedial actions for the dataset,blocking the dataset from being read for at least a predefined timeperiod to prevent potential data leaks and/or data breaches, and/or anyother suitable operation as may serve a particular implementation.

The systems and methods described herein are advantageous in a number oftechnical respects. For example, as described herein, the monitoringsystem may detect the noncompliance of the dataset stored at rest in thestorage system with the compliance ruleset currently in effect. As aresult, the monitoring system may perform timely operations to bring thedataset back in compliance with the compliance ruleset as required bythe compliance policy.

Moreover, the attribute model used to determine the compliance level ofthe dataset with the compliance ruleset may, in some cases, berelatively smaller in size and/or complexity than the dataset itself.Therefore, as described herein, use of the attribute model to determinea compliance level of the dataset with a compliance ruleset may requirerelatively less processing time and/or computing resources compared toscenarios in which data items (e.g., files, data blocks, data objects,and/or any other discrete unit of data) included in the dataset itselfhave to be rescanned to determine the compliance level.

Furthermore, the monitoring system may store various compliance rulesetsassociated with the dataset. The monitoring system may also storecorresponding compliance levels of the dataset with the compliancerulesets. Accordingly, the monitoring system can facilitate theevaluation of various compliance rulesets applied to the dataset. Thesecompliance rulesets may be enforced during different time periods, fordifferent geographical areas, by different government agencies and/ororganizations, etc.

While the monitoring system is described herein in a context ofmonitoring compliance of datasets stored at rest with a complianceruleset, the monitoring system may also be used to monitor compliance ofdatasets belonging to other types of data (e.g., data in use, data intransit, etc.) with a compliance ruleset. Other advantages and benefitsof the systems and methods described herein will be made apparentherein.

FIG. 4 illustrates an exemplary system 400 in accordance with someembodiments of the present disclosure. As depicted in FIG. 4 , system400 may include a storage system 410 and a monitoring system 420.

Storage system 410 may be implemented by any of the storage systems,devices, and/or components described herein. In some embodiments,storage system 410 may be an on-premises storage system (e.g., a localstorage system located on-site at a facility of an organization), acloud-based storage system (e.g., a storage system located on a cloudserver of a cloud services provider), or any combination thereof.

In some embodiments, storage system 410 may store one or more datasetsat rest as described herein. In some examples, a dataset stored bystorage system 410 may be associated with an entity (e.g., a company, afinancial entity, a healthcare organization, etc.) that has to complywith one or more compliance policies with respect to datasets stored atrest as described herein.

In some embodiments, monitoring system 420 may include, withoutlimitation, a memory 422 and a processor 424 selectively andcommunicatively coupled to one another as depicted in FIG. 4 . Memory422 and processor 424 may each include or be implemented by hardwareand/or software components (e.g., processors, memories, communicationinterfaces, instructions stored in memory for execution by theprocessors, etc.). In some embodiments, memory 422 and/or processor 424may be distributed between multiple devices and/or multiple locations asmay serve a particular implementation. In some examples, memory 422and/or processor 424 may be implemented by one or more componentsincluded in storage system 410 and/or applications executed by storagesystem 410. Additionally or alternatively, memory 422 and/or processor424 may be implemented by one or more components configured tocommunicate with storage system 410 by way of a network and/or othersuitable connection.

Memory 422 may maintain (e.g., store) executable data used by processor424 to perform any of the operations described herein. For example,memory 422 may store instructions 426 that may be executed by processor424 to perform any of the operations described herein. Instructions 426may be implemented by any suitable application, software, code, and/orother executable data instance. Memory 422 may also maintain any datareceived, generated, managed, used, and/or transmitted by processor 424.Memory 422 may additionally maintain any other suitable type of data asmay serve a particular implementation.

Processor 424 may be configured to perform (e.g., execute instructions426 stored in memory 422 to perform) various processing operationsdescribed herein. References herein to operations performed bymonitoring system 420 may be understood to be performed by processor424.

FIGS. 5A and 5B respectively depict a diagram 500 and a diagram 550illustrating example implementations of monitoring system 420 inrelation to storage system 410. As depicted in FIG. 5A, monitoringsystem 420 may be implemented within storage system 410. For example,monitoring system 420 may be implemented in the form of an applicationor a framework executed by storage system 410. Alternatively, asdepicted in FIG. 5B, monitoring system 420 may be implemented on acomputing device separate from storage system 410 and may becommunicatively coupled to storage system 410. For example, monitoringsystem 420 may be implemented in a cloud-based system and maycommunicate with storage system 410 via a communication network 552. Insome alternative embodiments, monitoring system 420 may be implementedby a combination of components within storage system 410 and componentsremote from storage system 410.

Monitoring system 420 may be configured to manage a compliance of thedata stored in storage system 410 with one or more compliance rulesetsassociated with one or more compliance policies as described herein.

To illustrate, FIG. 6 illustrates an exemplary method 600 that may beperformed by monitoring system 420 associated with storage system 410and/or any implementation thereof. Method 600 may be used alone or incombination with other methods described herein.

At operation 602, monitoring system 420 may obtain an attribute modelfor a dataset stored at rest within storage system 410. Monitoringsystem 420 may obtain the attribute model in any suitable manner. Forexample, monitoring system 420 may generate the attribute model of thedataset. As another example, monitoring system 420 may previouslygenerate the attribute model of the dataset when the dataset is ingestedinto storage system 410 and then retrieve data representative of theattribute model when needed as described herein.

The attribute model may indicate one or more attributes of the dataset.For example, the attribute model of the dataset may indicate dataattributes (e.g., data type, encryption information, accessibility,retention policy, etc.) of one or more data items included in thedataset. Examples of this are described herein.

At operation 604, monitoring system 420 may determine, based on theattribute model, a compliance level of the dataset with a complianceruleset associated with a compliance policy set forth by an entity(e.g., a government agency or an organization).

As described herein, the compliance ruleset may include one or morecompliance rules with which the dataset is required to comply to satisfythe compliance policy. In some embodiments, the compliance ruleset mayinclude rules corresponding to an up-to-date version of a compliancepolicy being enforced by the entity. Alternatively, the complianceruleset may include rules corresponding to one or more out-of-dateversions of the compliance policy (e.g., versions of the compliancepolicy as enforced by the entity in previous time periods).

Exemplary manners in which monitoring system 420 may determine acompliance level based on an attribute model are described herein.

At operation 606, monitoring system 420 may perform, based on thecompliance level of the dataset, an operation with respect to thedataset. For example, if the compliance level of the dataset is lowerthan a predefined compliance level threshold, monitoring system 420 maydetermine that the dataset does not comply with the compliance rulesetspecified by the compliance policy. Accordingly, monitoring system 420may perform one or more operations such as presenting a noncompliancenotification associated with the dataset, presenting a recommendationincluding one or more remedial actions to conform the dataset with thecompliance ruleset, automatically perform the remedial actions for thedataset, etc. These and other operations that may be performed based onthe compliance level of the dataset are described herein.

As mentioned, the attribute model of the dataset being used to determinethe compliance level of the dataset with the compliance ruleset may begenerated when the dataset is ingested into storage system 410. Forexample, the dataset may be transmitted to storage system 410 to bestored at rest within storage system 410. When the dataset is ingestedinto storage system 410, the dataset may also be provided to monitoringsystem 420 and monitoring system 420 may generate an attribute model forthe dataset. As an example, monitoring system 420 may generate theattribute model for the dataset when storage system 410 receives and/orstores the dataset. Alternatively, monitoring system 420 may generatethe attribute model for the dataset within a predefined time periodsince storage system 410 receives and/or stores the dataset.

In some embodiments, to generate the attribute model for the dataset,monitoring system 420 may scan the entire dataset and determine one ormore data attributes of one or more data items in the dataset. The dataattributes of a data item in the dataset may include a data type of thedata item, encryption information of the data item, an accessibility ofthe data item, a retention policy of the data item, an operation logconfiguration of the data item, etc. Other types of data attributes ofthe data item are also possible and contemplated.

As used herein, a data type of a data item may indicate a type of datato which the data item is classified. Non-limiting examples of the datatype include, but are not limited to, a healthcare record, financialtransaction data, personal identifiable information (e.g., emailaddress, social security number, etc.), etc.

As used herein, encryption information of a data item may includevarious information about the data encryption applied to the data item.For example, the encryption information may indicate an encryptionalgorithm being used to encrypt the data item, a timestamp at which thedata item is encrypted, characteristics (e.g., key length, lifetime,etc.) of encryption keys being used to encrypt the data item, etc.

As used herein, an accessibility of a data item may specify accesspermissions of various users and/or user groups with regard to the dataitem. For example, the accessibility of the data item may be indicatedin an access control list including one or more entries. Each entry inthe access control list may specify a user or a user group that canaccess the data item and one or more operations (e.g., read operation,modify operation, copy operation, etc.) that the user or user group isallowed to perform on the data item.

As used herein, a retention policy of a data item may specify aretention period during which the data item cannot be deleted and/oroverwritten. Accordingly, the data item may be maintained in storagesystem 410 at least during the retention period.

As used herein, the operation log configuration of the data item mayspecify various information being collected for an operation performedon the data item. Non-limiting examples of collected information for anoperation include, but are not limited to, a type of the operation(e.g., read operation, write operation, copy operation, etc.),information identifying a user requesting the operation, a timestamp atwhich the operation occurs, etc.

In some embodiments, monitoring system 420 may use a machine learningmodel to determine the data attributes of the data items in the dataset.The machine learning model may be implemented using one or moresupervised and/or unsupervised learning algorithms. For example, themachine learning model may be implemented in the form of a linearregression model, a logistic regression model, a Support Vector Machine(SVM) model, and/or other learning models. Additionally oralternatively, the machine learning model may be implemented in the formof a neural network including an input layer, one or more hidden layers,and an output layer. Non-limiting examples of the neural networkinclude, but are not limited to, Convolutional Neural Network (CNN),Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM) neuralnetwork, etc. Other learning model architectures for implementing themachine learning model are also possible and contemplated.

In some embodiments, monitoring system 420 may input the dataset intothe machine learning model and the machine learning model may generatean output indicating data attributes of one or more data items in thedataset. In some embodiments, to determine the data attributes of thedata items in the dataset, the machine learning model may be subjectedto a training process performed by a training system. The trainingsystem may be implemented by a computing device, monitoring system 420,storage system 410, and/or any combination thereof. An example trainingsystem 700 for training a machine learning model is illustrated in FIG.7 .

As depicted in FIG. 7 , training system 700 may include a machinelearning model 702 and a feedback computing unit 704. In someembodiments, machine learning model 702 may be trained with a pluralityof training examples 706-1 . . . 706-n (commonly referred to herein astraining examples 706). As depicted in FIG. 7 , each training example706 may include a dataset 708 and target data attributes 710. In someembodiments, target data attributes 710 may include one or more dataattributes of one or more data items included in dataset 708. Forexample, for each data item in dataset 708, target data attributes 710may include an actual data type of the data item, an actual encryptioninformation of the data item, an actual accessibility of the data item,an actual retention policy of the data item, etc.

In some embodiments, to train machine learning model 702 with a trainingexample 706 in a training cycle, training system 700 may determine,using machine learning model 702, output data attributes 712 for adataset 708 in training example 706. For example, as depicted in FIG. 7, training system 700 may apply machine learning model 702 to dataset708, and machine learning model 702 may compute output data attributes712 of the data items in dataset 708. In some embodiments, output dataattributes 712 may include one or more data attributes of each data itemin dataset 708 that machine learning model 702 determines based on thedata item.

In some embodiments, training system 700 may compute a feedback value714 based on output data attributes 712 and target data attributes 710.For example, as depicted in FIG. 7 , training system 700 may provideoutput data attributes 712 determined by machine learning model 702 andtarget data attributes 710 included in training example 706 to feedbackcomputing unit 704. As described herein, output data attributes 712 mayinclude one or more data attributes of each data item in dataset 708that are determined by machine learning model 702, and target dataattributes 710 may include one or more actual data attributes of eachdata item in dataset 708.

In some embodiments, feedback computing unit 704 may compute feedbackvalue 714 based on output data attributes 712 and target data attributes710. For example, feedback value 714 may be a mean squared error betweenthe data attributes of the data items in output data attributes 712 thatare determined by machine learning model 702 and the actual dataattributes of the data items in target data attributes 710 that areincluded in training example 706. Other implementations for computingfeedback value 714 are also possible and contemplated.

In some embodiments, training system 700 may adjust one or more modelparameters of machine learning model 702 based on feedback value 714.For example, as depicted in FIG. 7 , training system 700 mayback-propagate feedback value 714 computed by feedback computing unit704 to machine learning model 702, and adjust the model parameters ofmachine learning model 702 based on feedback value 714. For example,training system 700 may adjust one or more values assigned to one ormore coefficients of machine learning model 702 based on feedback value714.

In some embodiments, training system 700 may determine whether the modelparameters of machine learning model 702 have been sufficientlyadjusted. For example, training system 700 may determine that machinelearning model 702 has been subjected to a predetermined number oftraining cycles. Therefore, training system 700 may determine thatmachine learning model 702 has been trained with a predetermined numberof training examples, and thus determine that the model parameters ofmachine learning model 702 have been sufficiently adjusted.

Additionally or alternatively, training system 700 may determine thatfeedback value 714 satisfies a predetermined feedback value threshold,and thus determine that the model parameters of machine learning model702 have been sufficiently adjusted.

Additionally or alternatively, training system 700 may determine thatfeedback value 714 remains substantially unchanged for a predeterminednumber of training cycles (e.g., a difference between the feedbackvalues computed in sequential training cycles satisfying a differencethreshold), and thus determine that the model parameters of machinelearning model 702 have been sufficiently adjusted.

In some embodiments, based on the determination that the modelparameters of machine learning model 702 have been sufficientlyadjusted, training system 700 may determine that the training process ofmachine learning model 702 is completed. Training system 700 may thenselect the current values of the model parameters to be the values ofthe model parameters in trained machine learning model 702.

In some embodiments, once machine learning model 702 is sufficientlytrained, machine learning model 702 may be implemented on monitoringsystem 420 to determine data attributes of data items in a dataset. Asdescribed herein, monitoring system 420 may input the dataset intomachine learning model 702, and machine learning model 702 may generatean output indicating the data attributes of the data items in thedataset.

Additionally or alternatively, machine learning model 702 may beimplemented not on monitoring system 420 but on a separate computingdevice (e.g., a cloud-based server, an on-premises computer, storagesystem 410, etc.) communicatively coupled to monitoring system 420. Inthis case, monitoring system 420 may transmit the dataset to theseparate computing device. The separate computing device may use machinelearning model 702 to generate an output for the dataset, and transmitthe output to monitoring system 420.

In some embodiments, instead of using machine learning model 702,monitoring system 420 may use other implementations and/or algorithms togenerate the attribute model including the data attributes of the dataitems in the dataset.

In some embodiments, once the attribute model of the dataset isgenerated, monitoring system 420 may store the attribute model inassociation with the dataset. For example, monitoring system 420 maystore the attribute model of the dataset within storage system 410 inwhich the dataset is stored at rest. Additionally or alternatively,monitoring system 420 may store the attribute model of the datasetwithin a different storage system that is separate from storage system410.

In some embodiments, monitoring system 420 may retrieve the attributemodel of the dataset from the storage system and use the attribute modelto efficiently determine a compliance level of the dataset with acompliance ruleset associated with a compliance policy. In someembodiments, as the dataset is stored within storage system 410,monitoring system 420 may determine the compliance level of the datasetwith the compliance ruleset at any time. For example, monitoring system420 may detect a change in the compliance policy in which a previouscompliance ruleset associated with the compliance policy is modifiedinto the compliance ruleset. In response to such detection, monitoringsystem 420 may determine the compliance level of the dataset with thecompliance ruleset using the attribute model of the dataset. Forexample, for each dataset previously ingested into storage system 410,monitoring system 420 may perform method 600 to determine the compliancelevel of the dataset with the compliance ruleset based on the attributemodel of the dataset, and perform one or more corresponding operationswith regard to the dataset accordingly.

As described herein, the compliance ruleset may include one or morecompliance rules with which the data items in the dataset are requiredto comply to satisfy the compliance policy. As an example, for acompliance ruleset associated with a compliance policy that is appliedto personal identifiable information, a compliance rule in thecompliance ruleset may require that a data item in the dataset isobtained with explicit permission from a user associated with the dataitem. Another compliance rule in the compliance ruleset may require thatthe data item is stored in an encrypted format and displayed in ageneralized and/or anonymized format.

As another example, for a compliance ruleset associated with acompliance policy that is applied to healthcare information, acompliance rule in the compliance ruleset may require that theaccessibility of a data item in the dataset is limited to one or moreusers or user groups (e.g., doctors, clinic staffs, etc.) related to thepatient associated with the data item. Another compliance rule in thecompliance ruleset may require that operations performed on the dataitem are monitored and recorded in an operation log, at least foroperations that result in the data item being replicated and/ormodified.

As another example, for a compliance ruleset associated with acompliance policy that is applied to financial information, a compliancerule in the compliance ruleset may require that a data item in thedataset is protected against security attacks with at least a predefinednumber of data protection layers (e.g., firewall, data encryption,etc.). Another compliance rule in the compliance ruleset may requirethat the data item is retained in the storage system for at least apredefined amount of time.

In some embodiments, to determine the compliance level of the datasetwith the compliance ruleset, monitoring system 420 may use the dataattributes of each data item in the dataset as indicated in theattribute model. For example, monitoring system 420 may evaluate thedata attributes of the data item against the compliance rules in thecompliance ruleset, thereby determining whether the data item satisfiesthe compliance ruleset. In some embodiments, monitoring system 420 maydetermine that one or more data items in the dataset are noncompliantwith the compliance ruleset, and determine the compliance level of thedataset with the compliance ruleset based on the one or more data items.For example, monitoring system 420 may compute the compliance level ofthe dataset with the compliance ruleset to be proportional (e.g.,inversely proportional) to a number of data items being noncompliantwith the compliance ruleset. As another example, the compliance level ofthe dataset with the compliance ruleset may be proportional (e.g.,inversely proportional) to a percentage of data items in the datasetthat are noncompliant with the compliance ruleset.

Thus, while the attribute model that indicates the data attributes ofthe data items in the dataset may be generated by scanning the datasetas described herein, monitoring system 420 may use the attribute modelof the dataset to determine the compliance level of the dataset with thecompliance ruleset without rescanning the dataset. Alternatively,monitoring system 420 may determine the compliance level of the datasetwith the compliance ruleset based on a partial rescan of the dataset.For example, monitoring system 420 may identify a data item noncompliantwith the compliance ruleset and perform a partial rescan of the datasetusing a Bloom filter to determine whether the data item is included inthe dataset. Accordingly, monitoring system 420 may avoid a full rescanof the entire dataset, and therefore the amount of processing time andcomputing resources being used to determine the compliance level of thedataset with the compliance ruleset can be reduced.

As described herein, once the compliance level of the dataset with thecompliance ruleset is determined, monitoring system 420 may perform anoperation with respect to the dataset based on the compliance level ofthe dataset with the compliance ruleset. For example, monitoring system420 may determine that the compliance level of the dataset with thecompliance ruleset satisfies a predefined compliance level threshold(e.g., the compliance level may be lower than the predefined compliancelevel threshold), and therefore determine that the dataset isnoncompliant with the compliance ruleset. Based on such determination,monitoring system 420 may perform an operation with respect to thedataset.

As an example of the operation performed with respect to the dataset,monitoring system 420 may present a noncompliance notificationassociated with the dataset. For example, monitoring system 420 maypresent the noncompliance notification to an authorized user (e.g., anauthenticated operator, administrator, manager, etc.) via a graphicaluser interface of a computing device. The noncompliance notification mayindicate that the dataset is no longer in compliant with the complianceruleset associated with the compliance policy. Accordingly, theauthorized user may be informed about the noncompliance of the datasetwith the compliance ruleset currently enforced by the correspondinggovernment agency and/or organization.

Additionally or alternatively, monitoring system 420 may present arecommendation including one or more remedial actions to conform thedataset with the compliance ruleset. For example, monitoring system 420may recommend one or more adjustments of the accessibility, theencryption, the retention policy, and/or other aspects of data items inthe dataset based on the compliance rules in the compliance ruleset tobring the dataset in compliance with the compliance ruleset.

Additionally or alternatively, monitoring system 420 may automaticallyperform the remedial actions for the dataset. For example, monitoringsystem 420 may automatically adjust the accessibility, the encryption,the retention policy, and/or other aspects of data items in the datasetbased on the compliance rules in the compliance ruleset. As a result,the noncompliance of the dataset with the compliance ruleset can beaddressed without user intervention.

In some embodiments, monitoring system 420 may use the attribute modelof the dataset to determine compliance levels of the dataset withvarious compliance rulesets associated with the compliance policy. Thesecompliance rulesets may be enforced during different time periods, fordifferent geographical areas, by different government agencies and/ororganizations, etc. In some embodiments, monitoring system 420 may storemultiple compliance rulesets associated with the compliance policy andcompliance levels of the dataset with the multiple compliance rulesetsin a storage system.

As an example, based on the attribute model of the dataset, monitoringsystem 420 may determine not only the compliance level of the datasetwith the compliance ruleset associated with the compliance policy butalso determine an additional compliance level of the dataset with anadditional compliance ruleset associated with the compliance policy.

Monitoring system 420 may perform an operation with respect to thedataset based on the compliance level of the dataset with the complianceruleset and further based on the additional compliance level of thedataset with the additional compliance ruleset. For example, theadditional compliance ruleset may be the previous compliance rulesetthat is modified into the compliance ruleset and causes thenoncompliance of the dataset with the compliance policy. In this case,monitoring system 420 may present the compliance ruleset, the additionalcompliance ruleset, and the compliance levels of the dataset with thetwo compliance rulesets to an authorized user, thereby facilitating theauthorized user in evaluating the difference between compliance rulesetand the additional compliance ruleset as well as evaluating thenoncompliance of the dataset with the compliance ruleset.

As another example, monitoring system 420 may generate a compliancegraph illustrating the compliance levels of the dataset with variouscompliance rulesets associated with the compliance policy over time.Monitoring system 420 may then present the compliance graph to theauthorized user for evaluation.

As another example, monitoring system 420 may identify, from multiplecompliance rulesets associated with the compliance policy and compliancelevels of the dataset with the multiple compliance rulesets, one or morecompliance rulesets and one or more corresponding compliance levels ofthe dataset based on a time period, a geographical area, and/or otherfactors as requested by an authorized user. Monitoring system 420 maythen present to the authorized user the one or more compliance rulesetsand the one or more corresponding compliance levels of the dataset asrequested.

In some embodiments, operations of a monitoring system such asmonitoring system 420 described herein may be collaboratively performedby a storage management system of a storage system (e.g., storage system410) and an orchestration system of a computing system coupled to thestorage system. The computing system may be configured to execute one ormore software applications, and the storage system may be used to storedata for the computing system. For example, the storage system may storedata related to computing operations performed by the computing systemto execute the one or more software applications. In some conventionalsystems, the computing system may access and/or interact with the datastored within the storage system. However, the computing system may notbe aware of one or more events that occur within the storage system. Asa result, the computing system is unable to perform operations inresponse to these storage system events.

A storage management system of a storage system described herein maycollaborate with an orchestration system of a computing systemassociated with the storage system such that the storage managementsystem may drive the orchestration system to organize and manage anexecution of an operation related to a compliance ruleset based on oneor more events within the storage system. As described herein, thestorage management system may detect an event within the storage system.For example, the storage management system may detect a change in anitem attribute of a data item stored in the storage system, aninteraction (e.g., a write operation, a modify operation, etc.) with adata item in the storage system, a change in a compliance ruleset thatis stored as a data item within the storage system and applied to one ormore datasets in the storage system, etc.

When detecting the event within the storage system, the storagemanagement system may determine an operation based on the event. Forexample, the storage management system may reference a mapping list anddetermine an operation (e.g., a serverless function, a statefulapplication, etc.) that is mapped to the event in the mapping list. Asdescribed herein, the operation may be related to a compliance rulesetassociated with a compliance policy. For example, the operation mayinclude determining a compliance level of a dataset related to the eventwith the compliance ruleset, generating an attribute model for thedataset that can be used to efficiently determine the compliance levelof the dataset with the compliance ruleset, etc. The storage managementsystem may then provide a notification of the operation to theorchestration system and the orchestration system may manage (e.g.,direct) the computing system to execute the operation.

Accordingly, the computing system may execute the operation related tothe compliance ruleset in response to the event within the storagesystem, even if the event is detected by the storage management systemof the storage system and the orchestration system of the computingsystem is unaware of the event. Thus, the computing system and thestorage system may operate in an integrated manner with regards to thecompliance ruleset associated with the compliance policy due to thecollaboration between the orchestration system of the computing systemand the storage management system of the storage system.

FIGS. 8A and 8B respectively illustrate diagrams 800 and 850 of examplesystems in accordance with some embodiments of the present disclosure.As depicted in FIGS. 8A and 8B, a system may include a computing system802 communicatively coupled to storage system 410 described herein.

In some embodiments, computing system 802 may implement one or moresoftware applications and may be configured to perform one or morecomputing operations to execute the software applications. To performthese computing operations, computing system 802 may interact withstorage system 410. For example, computing system 802 may read one ormore data items from storage system 410, write one or more data items tostorage system 410, and/or perform other operations (e.g., modifyoperations, copy operations, etc.) on one or more data items storedwithin storage system 410.

In some embodiments, computing system 802 may include an orchestrationsystem 804 configured to manage various computing operations performedby computing system 802. For example, orchestration system 804 mayassign tasks to various nodes (e.g., computing machines) of computingsystem 802, allocate computing resources for the software applicationsimplemented on computing system 802, assign storage volumes of storagesystem 410 to the nodes of computing system 802, etc.

In some embodiments, orchestration system 804 may include or may beimplemented by hardware and/or software components (e.g., processors,memories, communication interfaces, instructions stored in memory forexecution by the processors, etc.). In some embodiments, orchestrationsystem 804 may be implemented by one or more components (e.g., a masternode) of computing system 802 and/or applications executed by computingsystem 802. Additionally or alternatively, orchestration system 804 maybe implemented by one or more components configured to communicate withcomputing system 802 via a communication network and/or other suitableconnection.

In some embodiments, orchestration system 804 may be distinct fromstorage system 410 and/or a storage management system 810 of storagesystem 410. Therefore, while orchestration system 804 may assign one ormore storage volumes of storage system 410 to a particular node ofcomputing system 802 so that data stored in these storage volumes may beaccessible to the particular node, orchestration system 804 may beunaware of events occurring within storage system 410. For example,orchestration system 804 may not be aware of a change in an itemattribute (e.g., an ACL, a retention policy, etc.) of a data item storedin storage system 410, an interaction (e.g., a modify operation, adelete operation, etc.) with a data item stored in storage system 410, achange in a mounting status (e.g., mounting or unmounting) of a storagevolume in storage system 410, and/or other events within storage system410, especially if these events are caused by operations performed byother systems (e.g., other orchestration systems, operating systems,and/or computing systems) different from computing system 802.

In some embodiments, storage system 410 may be any of the storagesystems described herein. For example, storage system 410 may include afile system configured to store one or more data files, an object-basedstorage system configured to store one or more objects, and/or any othertypes of storage system. In some embodiments, storage system 410 may beconfigured to store various data such as data associated with thesoftware applications implemented on computing system 802 and/or othertypes of data that is stored at rest, in use, or in transit.

In some embodiments, storage system 410 may include a storage managementsystem 810 configured to manage various operations performed on or bystorage system 410 and/or monitor various events that occur withinstorage system 410. For example, storage management system 810 maydetect a change in an item attribute of a data item stored in storagesystem 410, an interaction with a data item stored in storage system410, a change in a mounting status of a storage volume in storage system410, and/or other events within storage system 410 described herein.

In some embodiments, storage management system 810 may include or may beimplemented by hardware and/or software components (e.g., processors,memories, communication interfaces, instructions stored in memory forexecution by the processors, etc.). In some embodiments, storagemanagement system 810 may be implemented within storage system 410.Additionally or alternatively, storage management system 810 may beimplemented on a different system (e.g., a system other than storagesystem 410) that is configured to communicate with storage system 410via a communication network and/or other suitable connection.

In some embodiments, storage management system 810 may be implemented ina centralized manner. For example, storage management system 810 may beimplemented as a single component within storage system 410 and maycommunicate with orchestration system 804 of computing system 802 via acommunication channel 814 as depicted in FIG. 8A. Alternatively, storagemanagement system 810 may be implemented in a distributed manner. Forexample, storage management system 810 may be implemented within storagesystem 410 and may include a software component 812 that resides withinorchestration system 804 of computing system 802 as depicted in FIG. 8B.

In some embodiments, software component 812 may be a specializedsoftware agent (e.g., a controller extension) associated with storagemanagement system 810 but located within orchestration system 804.Software component 812 may communicate with storage management system810 via a communication channel 818 and may be configured to perform oneor more operations of storage management system 810. For example,software component 812 may receive from storage management system 810 anotification of an event within storage system 410 and determine anoperation to be executed by computing system 802 based on the event.Software component 812 may then request orchestration system 804 toorganize and manage an execution of the operation by computing system802. It should be understood that one or more operations of storagemanagement system 810 described herein may be performed by softwarecomponent 812 and vice versa.

In some embodiments, computing system 802 and storage system 410described herein may be implemented in the form of a container systemand a storage system associated with the container system. In someembodiments, the container system may be configured to operate one ormore containerized applications. A containerized application (alsoreferred to herein as a container) may include a software applicationand an entire runtime environment of the software application bundledinto a single package. For example, the containerized application mayinclude source code of the software application and variousdependencies, libraries, and/or other components that are necessary foror otherwise used by the software application to operate. As a result,the containerized application may be abstracted away from a hostoperating system as a lightweight and portable package, and thereforethe containerized application can be uniformly deployed and consistentlyexecuted on different computing environments that use differentoperating systems and/or different infrastructures.

In some embodiments, the container system may run various containerizedapplications on one or more clusters. Each cluster may include one ormore computing machines on which the containerized applications may bedeployed and executed. In some embodiments, each computing machine thatforms the cluster may be a physical machine or a virtual machineimplemented on one or more computing devices (e.g., one or more physicalservers) included in an on-premises system (e.g., a local system locatedon-site at a facility of an organization), a cloud-based system (e.g., asystem located on a cloud server of a cloud services provider), or anycombination thereof. Such a computing machine may also be referred toherein as a node in the cluster.

In some embodiments, operations of various containerized applications onmultiple nodes of the cluster may be managed by an orchestration system,such as Kubernetes, Docker Swarm, etc. The orchestration system may alsomanage one or more clusters in the container system. In someembodiments, a cluster of a container system may be associated with astorage system that stores various types of data for one or more nodesin the cluster. Once a node joins the cluster, the node may be allowedto access the storage system and perform various operations on the datastored within the storage system.

Accordingly, when the system structures depicted in FIGS. 8A and 8B areused to implement the container system, computing system 802 may beimplemented as the container system, orchestration system 804 may beimplemented as the orchestration system that manages the containerizedapplications and/or the clusters in the container system, and storagesystem 410 may be implemented as the storage system associated with oneor more clusters in the container system.

FIG. 9 illustrates a diagram 900 of an example cluster 902 of acontainer system in accordance with some embodiments of the presentdisclosure. As depicted in FIG. 9 , cluster 902 may include a masternode 904 and one or more worker nodes 906-1 . . . 906-n (commonlyreferred to herein as worker nodes 906).

In some embodiments, master node 904 may be a control node configured tomanage worker nodes 906 of cluster 902. For example, master node 904 mayreceive from a user via a user interface (e.g., a graphical interface, acommand line interface, an orchestration system API, etc.) a user inputdefining a desired state of cluster 902. The desired state may specifyone or more containerized applications to be run on one or more nodes(e.g., worker nodes 906, master node 904, etc.) of cluster 902. Thedesired state may also specify a container image, a number of replica,and/or other configurations of the one or more containerizedapplications. Based on the desired state, master node 904 may performvarious management operations to automatically achieve and maintain thedesired state for cluster 902. Non-limiting examples of the managementoperations performed by master node 904 include, but are not limited to,assigning tasks to worker nodes 906, monitoring performance of workernodes 906, scheduling the containerized applications, allocatingcomputing resources for the containerized applications, scaling orremoving the containerized applications, load balancing and trafficrouting, etc. In some embodiments, master node 904 may represent theorchestration system of the container system, which in turn maycorrespond to orchestration system 804 of computing system 802 asdescribed herein.

As depicted in FIG. 9 , master node 904 may include a scheduler 908 anda component associated with a storage management system such as astorage management containerized application 910.

In some embodiments, scheduler 908 may be configured to facilitatecommunication between master node 904 and other nodes. For example,scheduler 908 may communicate with a scheduler agent 912 of a workernode 906 in cluster 902. Scheduler 908 may also communicate withscheduler 908 of a master node 904 in another cluster of the containersystem.

In some embodiments, the component associated with the storagemanagement system may perform one or more operations of the storagemanagement system that is configured to manage a storage systemassociated with cluster 902. As described herein, such a storage systemmay include one or more storage elements within the cluster and/or oneor more storage elements outside the cluster. For example, the storagesystem may include storage resources contributed by one or more nodeswithin cluster 902 and/or storage resources contributed by one or morenodes outside cluster 902. Once the storage system is established,worker nodes 906 in cluster 902 may perform various operations (e.g.,read operation, write operation, copy operation, etc.) on the storagesystem.

In some embodiments, the component associated with the storagemanagement system may be implemented as storage management containerizedapplication 910 as depicted in FIG. 9 . Storage management containerizedapplication 910 may be a specialized containerized application deployedon master node 904 and/or worker nodes 906 and may be configured toperform one or more operations of the storage management system tomanage the storage system associated with cluster 902. Thus, whenmapping the container system to the system architectures depicted inFIGS. 8A and 8B, storage management containerized application 910 maycorrespond to software component 812 that is associated with storagemanagement system 810 of storage system 410 and resides withinorchestration system 804 of computing system 802. Alternatively, storagemanagement containerized application 910 may correspond to storagemanagement system 810 that manages storage system 410 but residesremotely from storage system 410.

In some embodiments, in addition to master node 904, cluster 902 mayalso include one or more worker nodes 906 managed by master node 904 asdepicted in FIG. 9 . Worker nodes 906 may include one or more storagenodes and one or more storage-less nodes. In some embodiments, thestorage nodes may contribute their storage resources to form the storagesystem associated with cluster 902 and may collaborate with one anotherto establish cluster 902. On the other hand, the storage-less nodes(also referred to as compute nodes) may not contribute their storageresources to the storage system of cluster 902 and may participate incluster 902 once cluster 902 is established by the storage nodes. Insome embodiments, cluster 902 may be implemented in a disaggregateddeployment mode in which the containerized applications can only run onthe compute nodes but not on the storage nodes of cluster 902.Alternatively, cluster 902 may be implemented in a hyperconvergeddeployment mode in which the containerized applications can run on boththe compute nodes and the storage nodes of cluster 902.

As depicted in FIG. 9 , worker node 906 may include a scheduler agent912, one or more containerized applications 914-1 . . . 914-n (commonlyreferred to as containerized applications 914), and a componentassociated with a storage management system such as storage managementcontainerized application 910.

In some embodiments, scheduler agent 912 may be configured to facilitatecommunication between worker node 906 and other nodes. For example,scheduler agent 912 may communicate with a scheduler agent 912 ofanother worker node 906 in cluster 902. Scheduler agent 912 may alsocommunicate with scheduler 908 of master node 904 in cluster 902.

In some embodiments, containerized applications 914 may be containerscorresponding to various software applications (e.g., cloud-nativeapplications) deployed and executed on worker node 906. In someembodiments, different worker nodes 906 in cluster 902 may implementdifferent containerized applications 914 and/or implement instances ofthe same containerized applications 914 as instructed by master node 904to achieve the desired state of cluster 902.

As described herein, storage management containerized application 910may be a specialized containerized application configured to perform oneor more operations of the storage management system that manages thestorage system associated with cluster 902. As described herein, thestorage system associated with cluster 902 may include storage resourcesof one or more storage elements within cluster 902. For example, astorage node participating in cluster 902 may include one or morestorage devices (e.g., storage disks, storage drives, etc.) 920 and thestorage system may include storage space of storage devices 920-1 . . .920-n (commonly referred to as storage devices 920) contributed by oneor more storage nodes. Additionally or alternatively, the storage systemmay include storage resources of one or more storage elements outsidecluster 902. For example, the storage system may include storage spaceof storage devices 920 contributed by storage nodes in other clusters orinclude storage space provided by a different storage system (e.g., acloud-based storage system).

In some embodiments, storage management containerized application 910may perform various operations to create and manage the storage systemassociated with cluster 902. For example, storage managementcontainerized application 910 may aggregate the storage space of storagedevices 920 associated with the storage nodes of cluster 902 into astorage pool 922 as depicted in FIG. 9 . Storage pool 922 may includemultiple storage devices 920 that have the same or different storagetypes (e.g., SSD, HDD, NVMe, etc.) and/or storage capacities.Alternatively, storage management containerized application 910 mayclassify storage devices 920 of the storage nodes based on their storagetypes and/or storage capacities. Storage management containerizedapplication 910 may then aggregate storage devices 920 that have thesame storage type and/or the same storage capacity into a separatestorage pool 922. Accordingly, the storage system associated withcluster 902 may include multiple storage pools 922 in which each storagepool 922 may correspond to a particular storage type and/or a particularstorage capacity, and therefore storage devices 920 can be managedefficiently.

In some embodiments, storage management containerized application 910may virtualize one or more storage pools 922 into one or more virtualvolumes 924-1 . . . 924 n (commonly referred to herein as virtualvolumes 924 or storage volumes 924) as depicted in FIG. 9 . Each virtualvolume 924 may include storage space of storage devices 920 that areassociated with the same storage node and/or storage space of storagedevices 920 that are associated with different storage nodes. Storagemanagement containerized application 910 may also manage the mountingand unmounting of virtual volumes 924 to worker nodes 906 of cluster902. In some embodiments, when a virtual volume 924 is mounted to aworker node 906, worker node 906 may access virtual volume 924 andperform various operations (e.g., read operation, write operation,delete operation, etc.) on data stored in virtual volume 924. Whenvirtual volume 924 is unmounted from worker node 906, worker node 906may not access virtual volume 924 and cannot perform any operation onthe data stored in virtual volume 924.

As used herein, the storage system associated with cluster 902 may referto one or more storage pools 922 which include storage devices 920 ofthe storage nodes that provide storage resources for cluster 902.Alternatively, the storage system associated with cluster 902 may referto one or more virtual volumes 924 that are virtualized from storagedevices 920 included in one or more storage pools 922. In someembodiments, one or more storage pools 922 and one or more virtualvolumes 924 may represent the storage system of the container system inwhich the data may be stored and interacted with by master node 904 andworker nodes 906 of cluster 902. In some embodiments, cluster 902 mayrepresent computing components of the container system.

Accordingly, when mapping the container system to the systemarchitecture depicted in FIGS. 8A and 8B, cluster 902 may correspond tocomputing system 802, and master node 904 of cluster 902 may correspondto or implement orchestration system 804 of computing system 802 asdescribed herein. With such mapping, one or more storage pools 922and/or one or more virtual volumes 924 may correspond to storage system410, and storage management containerized application 910 may correspondto storage management system 810 or correspond to software component 812associated with storage management system 810 as described herein.

FIG. 10 illustrates a diagram 1000 of an example worker node 906. Asdepicted in FIG. 10 , worker node 906 may include a container enginelayer 1002, an operating system (OS) layer 1004, and a server layer1006. Worker node 906 may also include one or more containerizedapplications 914 and storage management containerized application 910 asdescribed herein. In some embodiments, worker node 906 may be acomputing machine (e.g., a physical machine or a virtual machine) andmay be implemented on one or more computing devices (e.g., one or morephysical servers) as described herein.

Container engine layer 1002 may provide a standardized interface forimplementing containerized applications 914. For example, containerengine layer 1002 may abstract various tasks related to data storage,networking, resource management, and/or other operations from theoperating system and provide a set of APIs to perform these tasks.Container engine layer 1002 may be implemented in any suitable way,including as an OS virtualization layer, a virtualization layer, or ahypervisor configured to provide an interface for implementingcontainerized applications 914.

OS layer 1004 may provide a standardized interface for container enginelayer 1002 to interact with server layer 1006. The standardizedinterface for communicating with server layer 1006 may be based on theOS (e.g., Linux, Microsoft Windows, etc.) of the physical machine or thevirtual machine implementing worker node 906. In some embodiments, OSlayer 1004 may also perform other OS-related functionalities.

Server layer 1006 may manage various hardware components (memory,storage devices 920, communication unit, etc.) of one or more physicalcomputing devices on which worker node 906 resides. Server layer 1006may also provide a standardized interface for OS layer 1004 to interactwith these hardware components.

In some embodiments, the separation of the computing environment ofworker node 906 into container engine layer 1002, OS layer 1004, andserver layer 1006 may facilitate the configuration of such computingenvironment and also facilitate the interactions of containerizedapplications 914 with the computing environment at different levels.Accordingly, containerized applications 914 may be flexibly deployed andexecuted on different worker nodes 906 even if these worker nodes 906have different OS and/or different hardware infrastructure.

As depicted in FIG. 10 , worker node 906 may include one or morecontainerized applications 914 corresponding to one or more softwareapplications. In some embodiments, containerized application 914 may begranted a limited permission that allows containerized application 914to directly communicate only with container engine layer 1002 using astandardized interface provided by container engine layer 1002.Container engine layer 1002 may then relay such communication to OSlayer 1004 and/or to other containerized applications 914.

As depicted in FIG. 10 , worker node 906 may also include storagemanagement containerized application 910. As described herein, storagemanagement containerized application 910 may be a specializedcontainerized application configured to perform one or more operationsof a storage management system that manages the storage system ofcluster 902 in which worker node 906 participates. Storage managementcontainerized application 910 may be an example embodiment of storagemanagement system 810 of storage system 410 as described herein, andtherefore operations performed by storage management system 810described herein may be performed by storage management containerizedapplication 910.

In some embodiments, while containerized applications 914 may be allowedto communicate only with container engine layer 1002 as described above,storage management containerized application 910 may be allowed tocommunicate with container engine layer 1002, OS layer 1004, serverlayer 1006, and/or the hardware components of the one or more physicalcomputing devices on which worker node 906 resides. With such privilegedpermission, storage management containerized application 910 may be ableto efficiently manage various operations on the storage system ofcluster 902 that is created from the storage resources of one or moreworker nodes 906.

As an example, a containerized application 914 on worker node 906 mayinitiate a write request to write a data item on a virtual volume 924 inthe storage system of cluster 902. Containerized application 914 maytransmit the write request to container engine layer 1002 using thestandardized interface provided by container engine layer 1002, andcontainer engine layer 1002 may forward the write request to storagemanagement containerized application 910. Based on the write request,storage management containerized application 910 may interact withstorage devices 920 of worker node 906 that correspond to the virtualvolume and/or interact with other worker nodes 906 (e.g., storage nodes)that have their storage devices 920 corresponding to the virtual volumeto execute the write request.

Thus, computing system 802 including orchestration system 804 andstorage system 410 including storage management system 810 may beimplemented in various manners described above. In some embodiments,storage management system 810 may collaborate with orchestration system804 so that storage management system 810 may drive orchestration system804 to organize and manage an operation related to a compliance rulesetbased on an event within storage system 410. To illustrate, FIG. 11shows an exemplary method 1100 that may be performed by storagemanagement system 810 of storage system 410. Method 1100 may also beperformed in full or in part by any implementation and/or component ofstorage management system 810 such as storage management containerizedapplication 910, software component 812, or other suitableimplementations and/or components. Method 1100 may be used alone or incombination with other methods described herein.

At operation 1102, storage management system 810 may detect an eventwithin storage system 410. For example, storage management system 810may detect a change in an item attribute of a data item stored instorage system 410, an interaction with a data item stored in storagesystem 410, a change in a mounting status of a storage volume in storagesystem 410, and/or other events that occur within storage system 410.

At operation 1104, storage management system 810 may determine anoperation based on the event. For example, storage management system 810may determine one or more serverless functions, stateful applications,processing tasks (e.g., jobs), and/or other operations corresponding tothe event in a mapping list. In some embodiments, the operation may berelated to a compliance ruleset associated with a compliance policy. Forexample, the operation may include determining a compliance level of adataset related to the event with the compliance ruleset, generating anattribute model for the dataset that can be used to efficientlydetermine the compliance level of the dataset with the complianceruleset as described herein, etc.

At operation 1106, storage management system 810 may provide anotification of the operation to orchestration system 804. Orchestrationsystem 804 may then manage an execution of the operation by computingsystem 802.

Accordingly, when storage management system 810 detects the event withinstorage system 410, storage management system 810 may causeorchestration system 804 to organize and manage an execution of theoperation related to the compliance ruleset by computing system 802.Thus, computing system 802 may be able to perform the operation relatedto the compliance ruleset in response to the event within storage system410, even if orchestration system 804 that manages computing system 802is itself unaware of the event occurring within storage system 410.Accordingly, due to the collaboration between orchestration system 804of computing system 802 and storage management system 810 of storagesystem 410, computing system 802 and storage system 410 may operate inan integrated manner with regards to the compliance ruleset associatedwith the compliance policy.

In some embodiments, to detect an event within storage system 410,storage management system 810 may monitor operations performed onstorage devices (e.g., storage devices 920) of storage system 410,operations performed on storage volumes (e.g., virtual volumes 924) ofstorage system 410, and/or operations performed on data items (e.g.,data files, objects, etc.) stored within storage system 410.Accordingly, storage management system 810 may detect an event thatoccurs within storage system 410. Non-limiting examples of the eventinclude, but are not limited to, an attribute change indicating a changein an item attribute (e.g., an ACL, a retention policy, etc.) of a dataitem stored in storage system 410, an interaction (e.g., a readoperation, a write operation, a modify operation, a delete operation,etc.) with a data item stored in storage system 410, a change in acompliance ruleset that is stored as a data item within in storagesystem 410 and applied to one or more datasets in storage system 410, amounting of a storage device or a storage volume in storage system 410to a node (e.g., worker nodes 906, master node 904) in computing system802, an unmounting of a storage device or a storage volume in storagesystem 410 from a node in computing system 802, etc. Other types ofevents in a storage system are also possible and contemplated.

As described herein, when storage management system 810 detects theevent within storage system 410, storage management system 810 maydetermine an operation based on the event and provide a notification ofthe operation to orchestration system 804. In some embodiments, theoperation may be related to a compliance ruleset associated with acompliance policy. For example, the operation may include determining acompliance level of a dataset related to the event with the complianceruleset, generating an attribute model for the dataset that can be usedto efficiently determine the compliance level of the dataset with thecompliance ruleset, etc.

Alternatively, instead of determining the operation based on the eventand notifying orchestration system 804 about the operation, storagemanagement system 810 may transmit a notification of the event toorchestration system 804 and orchestration system 804 may then determinethe operation based on the event. Alternatively, storage managementsystem 810 may transmit a notification of the event to softwarecomponent 812 that is associated with storage management system 810 andresides within orchestration system 804. Software component 812 may thendetermine the operation based on the event and communicate the operationto orchestration system 804. Other implementations to determine and/orinform orchestration system 804 about the operation so thatorchestration system 804 can organize and manage the execution of theoperation by computing system 802 are also possible and contemplated.

In some embodiments, to determine the operation based on the event,storage management system 810 may rely on a mapping list including oneor more mappings. Each mapping may map an event to one or moreoperations to be performed in response to an occurrence of the event instorage system 410. In some embodiments, a mapping in the mapping listmay be defined by a user. For example, the user may specify an event andone or more operations corresponding to the event for the mapping via auser interface (e.g., a graphical interface, a command line interface, aconfiguration API, etc.). In some embodiments, the mapping list may bestored within storage system 410 and a change of a mapping in themapping list may be considered an event within storage system 410 andcan be detected by storage management system 810.

In some embodiments, when storage management system 810 detects an eventwithin storage system 410, storage management system 810 may referencethe mapping list and determine one or more operations that are mapped tothe event in the mapping list. Storage management system 810 may thengenerate a notification specifying the one or more operations andprovide the notification to orchestration system 804. Upon receiving thenotification, orchestration system 804 may organize and manage anexecution of the operations by computing system 802 as described herein.

In some embodiments, the operation corresponding to the event may be aserverless function, a stateful application, and/or a processing task(e.g., a job in the orchestration system of the container system) thatis configured to perform one or more predefined actions related to acompliance ruleset associated with a compliance policy when executed bycomputing system 802. For example, the serverless function, the statefulapplication, or the processing task may be configured to determine acompliance level of a dataset related to the event with the complianceruleset, generate an attribute model for the dataset that can be used toefficiently determine the compliance level of the dataset with thecompliance ruleset, and/or perform other predefined actions related tothe compliance ruleset in response to the event within storage system410.

As used herein, a serverless function may be a temporary function thatcan be dynamically instantiated and only exists for a limited timeperiod to perform one or more particular tasks. The serverless functionmay terminate when the particular tasks are completed. In someembodiments, the serverless function may be implemented in the form of alightweight containerized application (e.g., a container) in whichhardware resources, network capability, and other infrastructure for theserverless function to operate are provided by a serverless computingservice provider. The serverless computing service provider may alsomanage data associated with an execution of the serverless function. Asa result, the serverless function may not use resources of physicalcomputing devices in computing system 802 when the serverless functionis triggered by computing system 802, and therefore the serverlessfunction is considered serverless. Due to its serverless nature, theserverless function (e.g., a lambda function) may be operable ondifferent systems that have different operating systems and/or differentinfrastructures and can be run at any time.

In some embodiments, the serverless function may need to access storagesystem 410 to obtain data being used in its particular tasks fromstorage system 410 and/or to store its processing results within storagesystem 410. To provide the serverless function with necessary access tostorage system 410, orchestration system 804 of computing system 802 maycommunicate with storage management system 810 of storage system 410 togrant a temporary certificate to the serverless function. The temporarycertificate may provide the serverless function with a limited access toone or more storage locations within storage system 410. For example,storage management system 810 may replicate the data items being used bythe serverless function to a particular directory and the temporarycertificate may allow the serverless function to only read data from theparticular directory. The temporary certificate may also allow theserverless function to only write data to another directory such as adesignated sandbox. Once the serverless function completes itsparticular tasks and terminates, storage management system 810 mayautomatically revoke or invalidate the temporary certificate granted tothe serverless function.

In some embodiments, instead of or in addition to the serverlessfunction, the operation corresponding to the event within storage system410 may be a processing task (e.g., a job) similar to the serverlessfunction but initiated and managed by orchestration system 804 ofcomputing system 802. Additionally or alternatively, the operation maybe a stateless application that processes each request based only oninformation provided with the request. Additionally or alternatively,the operation may be a stateful application, which requires one or morereferences to state data associated with previous operations performedby the stateful application to process a request. Other types ofoperations to be executed in response to the event within storage system410 are also possible and contemplated.

As described herein, once the operation corresponding to the eventwithin storage system 410 is determined, storage management system 810may generate a notification specifying the operation. For example, thenotification may specify the serverless function, the processing task(e.g., job), the stateless application, and/or the stateful applicationto be executed by computing system 802 in response to the event withinstorage system 410. Storage management system 810 may then transmit thenotification to orchestration system 804 and orchestration system 804may organize and manage an execution of the serverless function, theprocessing task, the stateless application, and/or the statefulapplication by computing system 802 as described herein.

As a first example, storage management system 810 may detect an eventwithin storage system 410 in which the event includes one or moreinteractions (e.g., write operation, copy operation, delete operation,etc.) with one or more data items of a dataset stored in storage system410. Storage management system 810 may reference the mapping list anddetermine that the event is mapped to a serverless function in themapping list. The serverless function may be configured to determine acompliance level of the dataset with a compliance ruleset that the dataitems in the dataset are required to comply with to satisfy a compliancepolicy.

As described herein, storage management system 810 may generate anotification specifying the serverless function and transmit thenotification to orchestration system 804. Upon receiving thenotification specifying the serverless function, orchestration system804 may organize and manage an execution of the serverless function bycomputing system 802. For example, orchestration system 804 may assignthe serverless function to a worker node 906 of computing system 802.Worker node 906 may instantiate an instance of the serverless functionand execute the instance to determine the compliance level of thedataset with the compliance ruleset. In this example, the serverlessfunction may determine data attributes of the data items beinginteracted with in the event, and update an attribute model of thedataset to reflect the data attributes of these data items. Theserverless function may then determine the compliance level of thedataset with the compliance ruleset using the updated attribute model ofthe dataset as described herein, and write the compliance level of thedataset as an execution output to a predefined storage location (e.g., adesignated sandbox) within storage system 410.

Once the compliance level of the dataset is written to the predefinedstorage location within storage system 410, storage management system810 may obtain and analyze the compliance level of the dataset. Forexample, storage management system 810 may evaluate the compliance levelof the dataset against a predefined compliance level threshold, anddetermine whether the dataset is still compliant with the complianceruleset associated with the compliance policy given the interactionswith one or more data items in the dataset.

Additionally or alternatively, storage management system 810 may detectthe compliance level of the dataset being written to the predefinedstorage location in storage system 410 as an additional event withinstorage system 410. Therefore, similar to when storage management system810 detects the event within storage system 410, storage managementsystem 810 may cause orchestration system 804 to organize and manage anexecution of an additional operation by computing system 802 in responseto the additional event. For example, the additional operation mayinclude evaluating the compliance level of the dataset to determinewhether the dataset is compliant with the compliance ruleset. If thedataset is noncompliant with the compliance ruleset, the additionaloperation may also include presenting a noncompliance notificationassociated with the dataset to an authorized user (e.g., anauthenticated operator, administrator, manager, etc.), performing one ormore remedial actions to conform the dataset with the complianceruleset, etc. Other types of additional operation are also possible andcontemplated.

As a second example, storage management system 810 may detect an eventwithin storage system 410 in which the event includes one or moreattribute changes associated with one or more data items of a datasetstored in storage system 410. An attribute change associated with a dataitem may indicate a change in an item attribute (e.g., an ACL, aretention policy, a total number of backup copies, etc.) of the dataitem. In some embodiments, storage management system 810 may referencethe mapping list and determine that the event is also mapped to theserverless function configured to determine a compliance level of thedataset with the compliance ruleset associated with the compliancepolicy.

Similar to the example above, storage management system 810 may generatea notification specifying the serverless function and transmit thenotification to orchestration system 804. Upon receiving thenotification specifying the serverless function, orchestration system804 may organize and manage an execution of the serverless function bycomputing system 802. In this example, the serverless function mayupdate an attribute model of the dataset to reflect the attributeschanges of the one or more data items in the dataset. The serverlessfunction may then determine the compliance level of the dataset with thecompliance ruleset using the updated attribute model of the dataset, andwrite the compliance level of the dataset as an execution output to thepredefined storage location (e.g., the designated sandbox) withinstorage system 410 as described herein.

Once the compliance level of the dataset is written to the predefinedstorage location within storage system 410, storage management system810 may obtain and analyze the compliance level of the dataset. Forexample, storage management system 810 may evaluate the compliance levelof the dataset against the predefined compliance level threshold, anddetermine whether the dataset is still compliant with the complianceruleset associated with the compliance policy given the attributechanges associated with the one or more data items in the dataset.Additionally or alternatively, storage management system 810 may detectthe compliance level of the dataset being written to the predefinedstorage location in storage system 410 as an additional event withinstorage system 410. Accordingly, storage management system 810 may causeorchestration system 804 to organize and manage an execution of anadditional operation by computing system 802 in response to theadditional event as described herein.

As a third example, storage management system 810 may detect an eventwithin storage system 410 in which the event includes a mounting of astorage volume 924 in storage system 410 to a node (e.g., worker nodes906, master node 904) in computing system 802 or an unmounting of astorage volume 924 in storage system 410 from a node in computing system802. Storage management system 810 may reference the mapping list anddetermine that the event is mapped to a stateful application in themapping list. The stateful application may be configured to determineone or more compliance levels of one or more datasets in storage volume924 with a compliance ruleset that the data items in the datasets arerequired to comply with to satisfy the compliance policy.

Similar to the examples above, storage management system 810 maygenerate a notification specifying the stateful application and transmitthe notification to orchestration system 804. Upon receiving thenotification specifying the stateful application, orchestration system804 may organize and manage an execution of the stateful application bycomputing system 802. In this example, the stateful application maygenerate an execution output indicating one or more datasets in storagevolume 924 that are noncompliant with the compliance ruleset associatedwith the compliance policy, and write the execution output to apredefined storage location (e.g., a directory) within storage system410. Storage management system 810 may then obtain and analyze theexecution output. For example, storage management system 810 may computethe amount of data noncompliant with the compliance ruleset in storagevolume 924 relative to the total amount of data stored in storage volume924 (e.g., 35%) to generate a compliance report for various storagevolumes 924 in storage system 410.

Additionally or alternatively, storage management system 810 may detectthe writing of the execution output that indicates the noncompliantdatasets in storage volume 924 to the predefined storage location instorage system 410 as an additional event within storage system 410.Accordingly, storage management system 810 may cause orchestrationsystem 804 to organize and manage an execution of an additionaloperation by computing system 802 in response to the additional event.For example, the additional operation may include presenting anoncompliance notification associated with the noncompliant datasets instorage volume 924 to an authorized user, performing one or moreremedial actions to conform the noncompliant datasets in storage volume924 with the compliance ruleset, etc. Other types of additionaloperation are also possible and contemplated.

As a fourth example, storage management system 810 may detect an eventwithin storage system 410 in which a previous compliance rulesetassociated with a compliance policy is modified into a complianceruleset. In some embodiments, the compliance rulesets associated withthe compliance policy may be stored as a data item within storage system410 and may be automatically and/or manually updated as the compliancepolicy changes. As a result, storage management system 810 may detect achange in this data item as an event within storage system 410, therebydetecting a modification of the compliance rulesets associated with thecompliance policy. In some embodiments, storage management system 810may reference the mapping list and determine that the event is mapped toa stateful application in the mapping list. The stateful application maybe configured to determine one or more compliance levels of one or moredatasets stored in storage system 410 with the compliance rulesetcurrently enforced for the compliance policy.

Similar to the examples above, storage management system 810 maygenerate a notification specifying the stateful application and transmitthe notification to orchestration system 804. Upon receiving thenotification specifying the stateful application, orchestration system804 may organize and manage an execution of the stateful application bycomputing system 802. In this example, the stateful application maydetermine compliance levels of various datasets stored in storage system410 with the compliance ruleset currently in effect based on attributemodels of the datasets as described herein, and write the compliancelevels of the datasets as an execution output to a predefined storagelocation (a directory) within storage system 410.

Once the compliance levels of the datasets are written to the predefinedstorage location within storage system 410, storage management system810 may obtain and analyze the compliance levels of the datasets. Forexample, for each dataset in storage system 410, storage managementsystem 810 may evaluate the compliance level of the dataset against thepredefined compliance level threshold, and determine whether the datasetis compliant with the compliance ruleset associated with the compliancepolicy given the modification of the previous compliance ruleset to thecompliance ruleset.

Additionally or alternatively, storage management system 810 may detectthe compliance level of the dataset being written to the predefinedstorage location in storage system 410 as an additional event withinstorage system 410. Accordingly, storage management system 810 may causeorchestration system 804 to organize and manage an execution of anadditional operation by computing system 802 in response to theadditional event. For example, the additional operation may includeevaluating the compliance level of the dataset to determine whether thedataset is compliant with the compliance ruleset currently in effect. Ifthe dataset is noncompliant with the compliance ruleset, the additionaloperation may also include presenting a noncompliance notificationassociated with the dataset to an authorized user, performing one ormore remedial actions to conform the dataset with the complianceruleset, etc. Other types of additional operation are also possible andcontemplated.

As a fifth example, storage management system 810 may detect an eventwithin storage system 410 in which the event includes an ingestion of adataset into storage system 410. Storage management system 810 mayreference the mapping list and determine that the event is mapped to aserverless function in the mapping list. The serverless function may beconfigured to generate an attribute model for the dataset. As describedherein, the attribute model of the dataset may include data attributes(e.g., data type, accessibility, encryption information, etc.) of one ormore data items in the dataset and may be used to efficiently determinea compliance level of the dataset with a compliance ruleset associatedwith a compliance policy.

Similar to the examples above, storage management system 810 maygenerate a notification specifying the serverless function and transmitthe notification to orchestration system 804. Upon receiving thenotification specifying the serverless function, orchestration system804 may organize and manage an execution of the serverless function bycomputing system 802. In this example, the serverless function may scanthe data items in the dataset and determine the data attributes of thedata items to generate the attribute model for the dataset as describedherein. The serverless function may then write the attribute model ofthe dataset as an execution output to a predefined storage location(e.g., a directory) within storage system 410. Thus, the attribute modelof the dataset may be retrieved from storage system 410 and may be usedto efficiently determine a compliance level of the dataset with acompliance ruleset as described herein.

The examples described above are merely illustrative examples of anevent that may occur within storage system 410 and a correspondingoperation that storage management system 810 may cause orchestrationsystem 804 to organize and manage an execution by computing system 802.In some embodiments, the mapping between the event and the correspondingoperation may change. For example, storage management system 810 mayreceive a user input requesting that the event is no longer mapped tothe operation (e.g., a serverless function configured to perform a firstaction) but instead mapped to a different operation (e.g., a statefulapplication configured to perform a second action) in the mapping list.Due to this change in the mapping associated with the event, whenstorage management system 810 detects the event within storage system410, storage management system 810 may provide orchestration system 804with a notification specifying the different operation instead of theoperation previously mapped to the event. Thus, orchestration system 804may organize and manage an execution of the different operation bycomputing system 802 in response to the event detected within storagesystem 410.

In some embodiments, the collaboration between storage management system810 and orchestration system 804 described herein may result in asequence of events that occur within storage system 410 and operationsthat are managed by orchestration system 804 and executed by computingsystem 802 in response to these events. To illustrate, storagemanagement system 810 may detect an event within storage system 410,determine an operation based on the event, and provide a notification ofthe operation to orchestration system 804 as described herein. Uponreceiving the notification, orchestration system 804 may organize andmanage an execution of the operation by computing system 802 asdescribed herein. In some embodiments, the execution of the operation bycomputing system 802 may cause an additional event in storage system410. As a result, storage management system 810 may detect theadditional event associated with the operation within storage system410. Similar to when detecting the event within storage system 410,storage management system 810 may determine an additional operationbased on the additional event and provide an additional notification ofthe additional operation to orchestration system 804. Upon receiving theadditional notification, orchestration system 804 may manage anexecution of the additional operation by computing system 802.

As an example, storage management system 810 may detect a first eventwithin storage system 410. The first event may include one or moreinteractions with one or more data items of a dataset stored in storagesystem 410 as described in the first example above. Alternatively, thefirst event may include one or more attribute changes associated withone or more data items of a dataset stored in storage system 410 asdescribed in the second example above. As described herein withreference to the first example and the second example, storagemanagement system 810 may reference the mapping list and determine thatthe first event is mapped to the serverless function configured todetermine a compliance level of the dataset with the compliance rulesetassociated with the compliance policy. As described herein, storagemanagement system 810 may generate a notification specifying theserverless function and transmit the notification to orchestrationsystem 804.

Upon receiving the notification specifying the serverless function,orchestration system 804 may organize and manage an execution of theserverless function by computing system 802. For example, the serverlessfunction may update an attribute model of the dataset based on theinteractions or the attribute changes in the first event, determine thecompliance level of the dataset with the compliance ruleset using theupdated attribute model of the dataset, and write the compliance levelof the dataset as an execution output to the predefined storage location(e.g., a designated sandbox) within storage system 410 as describedherein. Accordingly, the execution of the serverless function bycomputing system 802 may result in a second event within storage system410 in which the compliance level of the dataset with the complianceruleset is stored within storage system 410.

When the second event occurs within storage system 410, storagemanagement system 810 may detect the second event. Storage managementsystem 810 may reference the mapping list and determine that the secondevent is mapped to a different serverless function that is configured toanalyze the compliance level of the dataset with the compliance ruleset.Similar to when detecting the first event, storage management system 810may generate a notification specifying the different serverless functionand provide a notification of the different serverless function toorchestration system 804. Orchestration system 804 may then manage anexecution of the different serverless function by computing system 802.

As described herein, the different serverless function executed bycomputing system 802 may analyze the compliance level of the datasetwith the compliance ruleset. For example, the different serverlessfunction may determine that the compliance level of the dataset with thecompliance ruleset satisfies a predefined compliance level threshold(e.g., the compliance level may be lower than the predefined compliancelevel threshold), and therefore determine that the dataset isnoncompliant with the compliance ruleset. Based on such determination,the different serverless function may perform an action with respect tothe dataset.

As an example of the action performed with respect to the dataset, thedifferent serverless function may present a noncompliance notificationassociated with the dataset to an authorized user via a graphical userinterface of a computing device as described herein. The noncompliancenotification may indicate that the dataset is no longer in compliantwith the compliance ruleset associated with the compliance policy.

Additionally or alternatively, the different serverless function maypresent a recommendation including one or more remedial actions toconform the dataset with the compliance ruleset as described herein. Forexample, the different serverless function may recommend one or moreadjustments of the accessibility, the encryption, the retention policy,and/or other aspects of data items in the dataset based on thecompliance rules in the compliance ruleset to bring the dataset incompliance with the compliance ruleset.

Additionally or alternatively, the different serverless function mayautomatically perform the remedial actions for the dataset as describedherein. For example, the different serverless function may automaticallyadjust the accessibility, the encryption, the retention policy, and/orother aspects of data items in the dataset based on the compliance rulesin the compliance ruleset. As a result, the noncompliance of the datasetwith the compliance ruleset can be addressed without user intervention.

Accordingly, in response to detecting the first event within storagesystem 410 in which one or more data items in the dataset are interactedwith and/or item attributes of one or more data items in the datasethave changed, storage management system 810 may direct orchestrationsystem 804 to organize and manage the execution of the serverlessfunction to determine a compliance level of the dataset with thecompliance ruleset associated with the compliance policy. The executionof the serverless function may cause the second event within storagesystem 410 in which the compliance level of the dataset with thecompliance ruleset is written to storage system 410. In response todetecting the second event within storage system 410, storage managementsystem 810 may direct orchestration system 804 to organize and managethe execution of the different serverless function to analyze thecompliance level of the dataset with the compliance ruleset, and performan action with respect to the dataset accordingly. This sequence ofevent detection by storage management system 810 and executionmanagement of a corresponding operation by orchestration system 804 maycontinue until an operation organized and managed by orchestrationsystem 804 does not cause a subsequent event within storage system 410and/or until an event within storage system 410 is not mapped to acorresponding operation in the mapping list.

As another example, storage management system 810 may detect a firstevent within storage system 410. The first event may include aningestion of a dataset into storage system 410 as described in the fifthexample above. As described herein with reference to the fifth example,storage management system 810 may reference the mapping list anddetermine that the first event is mapped to the serverless functionconfigured to generate an attribute model for the dataset. As describedherein, storage management system 810 may generate a notificationspecifying the serverless function and transmit the notification toorchestration system 804.

Upon receiving the notification specifying the serverless function,orchestration system 804 may organize and manage an execution of theserverless function by computing system 802. For example, the serverlessfunction may scan the data items in the dataset, determine the dataattributes of the data items to generate the attribute model for thedataset, and write the attribute model of the dataset as an executionoutput to the predefined storage location (e.g., a directory) withinstorage system 410 as described herein. Accordingly, the execution ofthe serverless function by computing system 802 may result in a secondevent within storage system 410 in which the attribute model of thedataset is stored within storage system 410.

When the second event occurs within storage system 410, storagemanagement system 810 may detect the second event. Storage managementsystem 810 may reference the mapping list and determine that the secondevent is mapped to a different serverless function that is configured touse the attribute model of the dataset to determine a compliance levelof the dataset with a compliance ruleset associated with a compliancepolicy. Similar to when detecting the first event, storage managementsystem 810 may generate a notification specifying the differentserverless function and provide a notification of the differentserverless function to orchestration system 804. Orchestration system804 may then manage an execution of the different serverless function bycomputing system 802. As described herein, the different serverlessfunction executed by computing system 802 may determine the compliancelevel of the dataset with the compliance ruleset based on the attributemodel of the dataset, and write the compliance level of the dataset asan execution output to a predefined storage location (e.g., a designatedsandbox) within storage system 410 as described herein.

Accordingly, in response to detecting the first event within storagesystem 410 in which the dataset is ingested into storage system 410,storage management system 810 may direct orchestration system 804 togenerate an attribute model for the dataset. The execution of theserverless function may cause the second event within storage system 410in which the attribute model of the dataset is written to storage system410. In response to detecting the second event within storage system410, storage management system 810 may direct orchestration system 804to organize and manage the execution of the different serverlessfunction to determine a compliance level of the dataset with acompliance ruleset based on the attribute model of the dataset. Thissequence of event detection by storage management system 810 andexecution management of a corresponding operation by orchestrationsystem 804 may continue until an operation organized and managed byorchestration system 804 does not cause a subsequent event withinstorage system 410 and/or until an event within storage system 410 isnot mapped to a corresponding operation in the mapping list.

As described herein, storage system 410 may be a file system configuredto store data files and a data item (e.g., a video, an image, etc.) maybe transmitted in multiple portions to storage system 410 to be storedwithin storage system 410. Because storage system 410 is a file system,each portion of the data item may be processed individually when theportion is ingested into storage system 410, instead of waiting for theentire data item to arrive at storage system 410 to start the processingon the complete data item as compared to other types of storage systemssuch as object-based storage systems. As a result, storage managementsystem 810 may detect an event that occurs within storage system 410with regards to a portion of the data item instead of the entire dataitem, and cause orchestration system 804 to organize and manage anexecution of a corresponding operation by computing system 802 inresponse to the event as described herein.

In the preceding description, various exemplary embodiments have beendescribed with reference to the accompanying drawings. It will, however,be evident that various modifications and changes may be made thereto,and additional embodiments may be implemented, without departing fromthe scope of the invention as set forth in the claims that follow. Forexample, certain features of one embodiment described herein may becombined with or substituted for features of another embodimentdescribed herein. The description and drawings are accordingly to beregarded in an illustrative rather than a restrictive sense.

What is claimed is:
 1. A method comprising: monitoring, by a storagemanagement system, at least one of data stored within a storage systemor storage volumes of the storage system, detecting, by the storagemanagement system based on the monitoring, an event that occurs withinthe storage system; determining, by the storage management system basedon accessing a mapping list that maps events to operations to beperformed in response to an occurrence of the events, an operation thatis to be performed in response to the occurrence of the event, theoperation related to a compliance ruleset associated with a compliancepolicy; and transmitting, by the storage management system, anotification of the operation to an orchestration system configured tomanage containerized applications executed within one or more clustersincluded in a container system, the notification directing theorchestration system to cause the container system to execute theoperation.
 2. The method of claim 1, wherein: the orchestration systemis distinct from the storage management system; and the orchestrationsystem is unaware of one or more events within the storage system. 3.The method of claim 1, wherein: the storage system is a file systemconfigured to store one or more data files.
 4. The method of claim 1,wherein: the determining of the operation includes determining, based onthe event, at least one of a serverless function, a processing task, ora stateful application associated with the compliance ruleset; and thenotification of the operation specifies the at least one of theserverless function, the processing task, or the stateful application tobe executed by the container system.
 5. The method of claim 1, wherein:the event that occurs within the storage system includes one or moreinteractions with one or more data items of a dataset stored in thestorage system; and the operation executed by the container systemincludes determining a compliance level of the dataset with thecompliance ruleset.
 6. The method of claim 1, wherein: the event thatoccurs within the storage system includes one or more attribute changesassociated with one or more data items of a dataset stored in thestorage system; and the operation executed by the container systemincludes determining a compliance level of the dataset with thecompliance ruleset.
 7. The method of claim 1, wherein: the event thatoccurs within the storage system includes a mounting of a storage volumein the storage system to a node in the container system or an unmountingof the storage volume in the storage system from the node in thecontainer system; and the operation executed by the container systemincludes determining one or more compliance levels of one or moredatasets stored in the storage volume with the compliance ruleset. 8.The method of claim 1, wherein: the event that occurs within the storagesystem includes a detection that a previous compliance rulesetassociated with the compliance policy is modified into the complianceruleset; and the operation executed by the container system includesdetermining one or more compliance levels of one or more datasets storedin the storage system with the compliance ruleset.
 9. The method ofclaim 1, wherein: the event that occurs within the storage systemincludes an ingestion of a dataset into the storage system; and theoperation executed by the container system includes generating anattribute model for the dataset, the attribute model being used todetermine a compliance level of the dataset with the compliance ruleset.10. The method of claim 1, further comprising: detecting, by the storagemanagement system, an additional event that occurs within the storagesystem, the additional event being associated with the operation;determining, by the storage management system, an additional operationbased on the additional event; and transmitting, by the storagemanagement system, an additional notification of the additionaloperation to the orchestration system configured to manage an executionof the additional operation by the container system.
 11. The method ofclaim 10, wherein: the operation executed by the container systemincludes generating an attribute model for a dataset; the additionalevent that occurs within the storage system includes a storing of theattribute model within the storage system; and the additional operationexecuted by the container system includes determining a compliance levelof the dataset with the compliance ruleset based on the attribute model.12. The method of claim 10, wherein: the operation executed by thecontainer system includes determining a compliance level of a dataset inthe storage system with the compliance ruleset; the additional eventthat occurs within the storage system includes a storing of thecompliance level of the dataset within the storage system; and theadditional operation executed by the container system includes analyzingthe compliance level of the dataset.
 13. The method of claim 12, whereinthe analyzing of the compliance level of the dataset includes:determining that the compliance level of the dataset satisfies acompliance level threshold; and performing, based on the determiningthat the compliance level of the dataset satisfies the compliance levelthreshold, an action with respect to the dataset.
 14. The method ofclaim 13, wherein the action with respect to the dataset includes one ormore of: presenting, to a user, a noncompliance notification associatedwith the dataset; presenting, to the user, a recommendation includingone or more remedial actions to conform the dataset with the complianceruleset; or automatically performing the one or more remedial actionsfor the dataset.
 15. A system comprising: a memory storing instructions;and a processor communicatively coupled to the memory and configured toexecute the instructions to: monitor at least one of data stored withina storage system or storage volumes of the storage system; detect, basedon the monitoring, an event that occurs within the storage system;determine, based on accessing a mapping list that maps events tooperations to be performed in response to an occurrence of the events,an operation that is to be performed in response to the occurrence ofthe event, the operation related to a compliance ruleset associated witha compliance policy; and transmit a notification of the operation to anorchestration system configured to manage containerized applicationsexecuted within one or more clusters included in a container system, thenotification directing the orchestration system to cause the containersystem to execute the operation.
 16. The system of claim 15, wherein:the orchestration system is distinct from the system; and theorchestration system is unaware of one or more events within the storagesystem.
 17. The system of claim 15, wherein: the storage system is afile system configured to store one or more data files.
 18. The systemof claim 15, wherein: the determining of the operation includesdetermining, based on the event, at least one of a serverless function,a processing task, or a stateful application associated with thecompliance ruleset; and the notification of the operation specifies theat least one of the serverless function, the processing task, or thestateful application to be executed by the container system.
 19. Thesystem of claim 15, wherein: the event that occurs within the storagesystem includes a detection that a previous compliance rulesetassociated with the compliance policy is modified into the complianceruleset; and the operation executed by the container system includesdetermining one or more compliance levels of one or more datasets storedin the storage system with the compliance ruleset.
 20. A non-transitorycomputer-readable medium storing instructions that, when executed,direct a processor of a computing device to: monitor at least one ofdata stored within a storage system or storage volumes of the storagesystem; detect, based on the monitoring, an event that occurs within thestorage system; determine, based on accessing a mapping list that mapsevents to operations to be performed in response to an occurrence of theevents, an operation that is to be performed in response to theoccurrence of the event, the operation related to a compliance rulesetassociated with a compliance policy; and transmit a notification of theoperation to an orchestration system configured to manage containerizedapplications executed within one or more clusters included in acontainer system, the notification directing the orchestration system tocause the container system to execute the operation.